{"as_of":"2026-08-10T06:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a27743ad6d664565640f8be14c44a190de18a1f3f9490001f5741e8fd2f426ad","coverage":[{"denominator":108,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T21:21:41.250911Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2509.08094/citation-record","integrity":"/paper/2509.08094/integrity","json":"/paper/2509.08094/citation-record.json","paper":"/paper/2509.08094"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.824537Z","title":"Introduction to combustion, volume 287","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.824537Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:a170b89e89b408081befe6b6faff936902e131b3db8707e97959b73cf1b2a320","observation_id":"aa444e31-776e-4914-a193-252fe9185bd3","resolution":{"observed_at":"2026-08-04T21:21:40.824537Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.829305Z","title":"Combustion","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.829305Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:834833beff735b3b6d68b4dd28f91ac53eb21604c2448f5162fc9dd40b1b5e48","observation_id":"25103ace-d56d-4147-8517-33104ce03547","resolution":{"observed_at":"2026-08-04T21:21:40.829305Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.833976Z","title":"Theoretical and numerical combustion","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.833976Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:6d75cba96aeeb8434d2e71775890cee1c115f83d3060fe387e38c04e1c783dda","observation_id":"69763ef0-3647-48bc-8498-6db0cf9986f3","resolution":{"observed_at":"2026-08-04T21:21:40.833976Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.838290Z","title":"Combustion","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.838290Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:32ab8395bc9a98345eec65661670affaf93fb63848a7aacf2a6d844500c95306","observation_id":"3df7822a-3e3b-4340-82c9-670754f1e4c7","resolution":{"observed_at":"2026-08-04T21:21:40.838290Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.842642Z","title":"Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.842642Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:bd6b7c0d9b43ceb7068c5fc8a1671101f6f7a315190d7e5263e35b5042c3b40d","observation_id":"f8a7ba10-29c0-49c6-b4f2-82bc70676b34","resolution":{"observed_at":"2026-08-04T21:21:40.842642Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.847309Z","title":"Physics-informed machine learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.847309Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:8f561ce7abea371fff19eb86797b82980e54a01c153136daa6f778f440344214","observation_id":"8070d0e9-59ec-41c0-8f12-3bc8a013dcbe","resolution":{"observed_at":"2026-08-04T21:21:40.847309Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.852241Z","title":"Scientific machine learning through physics–informed neu- ral networks: Where we are and what’s next","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.852241Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:098bdc2f12a4eee0b948cbd27f7bc128d39e547efa1d6b5a13dc19393786d917","observation_id":"13b2f49d-9df6-4632-a4bd-0cfacedd67e9","resolution":{"observed_at":"2026-08-04T21:21:40.852241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.857435Z","title":"A physics-informed deep convolutional neural network for simulating and predicting transient darcy flows in heterogeneous reservoirs without labeled data","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.857435Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:a36dacb5216a63f68ca26a07ddd0dfe67f9a4f02ec06f59a39f17e96e0b67f63","observation_id":"15cb29d2-8a64-482e-9322-9ce13e028815","resolution":{"observed_at":"2026-08-04T21:21:40.857435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.861940Z","title":"Physics informed integral neural network for dynamic modelling of solvent-based post-combustion co2 capture process","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.861940Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:5d1660ecda281ee9f78f0500f939904ce2b7581996ae1a31f684506ce29ae26e","observation_id":"ca5dd97a-bff9-42c1-a6c2-cc49e30a55ef","resolution":{"observed_at":"2026-08-04T21:21:40.861940Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.866183Z","title":"Model predictive control of diesel engine emissions based on neural network modeling","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.866183Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:50fc3c855113e44a610628fa0b754a37d6a99748e225157eb48bf273b2165d88","observation_id":"319757e4-da62-4616-90f7-1052b392736f","resolution":{"observed_at":"2026-08-04T21:21:40.866183Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.870367Z","title":"Crk-pinn: A physics-informed neural net- work for solving combustion reaction kinetics ordinary differential equations","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.870367Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:d5250fa29aaf2a9f0198b9546432aa3d6fb82a1c054a3abbba00ec44b193724d","observation_id":"aa3d7699-3239-4955-ae69-a3fc94123e5b","resolution":{"observed_at":"2026-08-04T21:21:40.870367Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.874699Z","title":"Exploring surface reaction mech- anism using a surface reaction neural network framework","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.874699Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:197170618e11b6041c1d2911b595f0f4a1c3760788095375ff337dec8cab81a7","observation_id":"3190bc76-4d1b-4f1b-bbe9-f55235eaf2d8","resolution":{"observed_at":"2026-08-04T21:21:40.874699Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.879204Z","title":"Ppinn: Parareal physics-informed neural network for time-dependent pdes","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.879204Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:fd2aa6fad0c2efa93c7a0b2800d73f56f0774061f73c150d4e388ab18aacb4c8","observation_id":"a13c04db-07f4-4c6c-9e9d-788173a014d1","resolution":{"observed_at":"2026-08-04T21:21:40.879204Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.883752Z","title":"An adaptive sampling method based on expected improvement function and residual gradient in pinns","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.883752Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:b2860c6222db72b227a576c7b0bf539425a32f653fc88e23bb4418adfb1f350b","observation_id":"4a0f9031-99b5-4dd4-a1e7-8b421f062999","resolution":{"observed_at":"2026-08-04T21:21:40.883752Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.887901Z","title":"Physics-informed neural net- works for turbulent combustion: Toward extracting more statistics and closure from point multiscalar measurements","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.887901Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:13d3d6ad4b39dfa7d421ba8868b5c2090c82e9904905c039a2dc9fee837f562c","observation_id":"025a4ab2-c439-4a45-ab1d-06e6f3c8e2ae","resolution":{"observed_at":"2026-08-04T21:21:40.887901Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.892294Z","title":"Physics-informed neural network for solving a one-dimensional solid mechanics problem","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.892294Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:1960dbedcbf1ad822ad1353d23ba946320ed35815f8fe730f2aff220453239f7","observation_id":"d23682f7-7d28-492c-bf32-c7cf5ac97b50","resolution":{"observed_at":"2026-08-04T21:21:40.892294Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.896464Z","title":"Physics-informed neural network solver for numerical analysis in geoengineering","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.896464Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:628c51ce1bcca2e05f8730dc92ecb92fdbbd2d5586059e2e51434f543ec15ec9","observation_id":"f58280c0-6235-4852-b4ca-61a6a5151854","resolution":{"observed_at":"2026-08-04T21:21:40.896464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.900823Z","title":"Understanding physics-informed neural networks: techniques, applications, trends, and challenges","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.900823Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:a5846be05377d1aae07d87e76c80519c905d92a8ed54ad47c46d9d165b673092","observation_id":"6ce89a22-148b-4f08-859c-c44308d895a9","resolution":{"observed_at":"2026-08-04T21:21:40.900823Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.905209Z","title":"Finite volume method network for the acceleration of unsteady computational fluid dynamics: Non-reacting and reacting flows","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.905209Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:3d39634eb2d0f81cc3559d9ed2b4dc1b99a688044ec1bc32e47d4bb980992085","observation_id":"25f8af85-384f-4b83-9288-0b85d0c309e6","resolution":{"observed_at":"2026-08-04T21:21:40.905209Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.909090Z","title":"Flamepinn-1d: Physics-informed neural networks to solve forward and inverse problems of 1d laminar flames","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.909090Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:b54790fce7e618b5184bb1dc2293b7d46b0ccfa41d313dc7d6f5e4634f886d50","observation_id":"6f4d7a4e-2d13-442f-adbc-71561318d8c6","resolution":{"observed_at":"2026-08-04T21:21:40.909090Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.00422","last_updated":"2024-10-01T06:00:49Z","snapshot_observed_at":"2026-07-06T19:25:06.162911Z","submitted_at":"2024-10-01T06:00:49Z","title":"Exploring Physics-Informed Neural Networks: From Fundamentals to Applications in Complex Systems","version":1},"cited_work":{"arxiv_id":"2410.00422","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.00422","snapshot_observed_at":"2026-08-04T21:21:41.649836Z","title":"Exploring Physics-Informed Neural Networks: From Fundamentals to Applications in Complex Systems","venue":"cs.CE","work_id":"d5f04d45-205b-489c-84af-1fcacccc8fbf","year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.913082Z"},"links":{"cited_paper":"/paper/2410.00422","citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:04921c568cc684a597640beda483793c6a11cfb6a312e2a5b5cdc4c9194f6b97","observation_id":"917b7cda-b5ff-4874-8336-d1685a721ad3","resolution":{"observed_at":"2026-08-04T21:21:41.681503Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.07557","last_updated":"2023-08-24T13:07:48Z","snapshot_observed_at":"2026-08-05T15:56:21.382380Z","submitted_at":"2023-02-15T09:51:56Z","title":"On the Generalization of PINNs outside the training domain and the Hyperparameters influencing it","version":2},"cited_work":{"arxiv_id":"2302.07557","doi":null,"metadata_source":"pith","pith_arxiv_id":"2302.07557","snapshot_observed_at":"2026-08-04T21:21:41.605382Z","title":"On the Generalization of PINNs outside the training domain and the Hyperparameters influencing it","venue":"cs.LG","work_id":"3f6b45eb-e4b8-4c2a-9aca-b38e1e0fd06b","year":2023},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.917305Z"},"links":{"cited_paper":"/paper/2302.07557","citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:3d28e02b9669d842c7b098c1f2610df81be24490183d2e43e1b6ef3db3bcbd81","observation_id":"18597b0c-4a8e-4ff5-b7bb-ef0989d8ce0d","resolution":{"observed_at":"2026-08-04T21:21:41.623191Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.921818Z","title":"Exploring physics-informed neural networks for the generalized nonlinear sine-gordon equation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.921818Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:263dafdc15452171669f38728e58a479b9411fc239035c46e1453a3002b98267","observation_id":"b6b8a128-b3dc-4207-b5ae-343694b6ba82","resolution":{"observed_at":"2026-08-04T21:21:40.921818Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.925702Z","title":"Generalization of PINNs for various boundary and initial conditions","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.925702Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:afd26aea587a7346d42f35d67ce43650e24065dee4949358f4b534f8645751e8","observation_id":"7ea40232-e09f-489c-a364-17587b4f42df","resolution":{"observed_at":"2026-08-04T21:21:40.925702Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.929513Z","title":"Estimates on the generalization error of physics-informed neural networks for approximating pdes","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.929513Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:111515c4d9baa45df1e49c17a7f09e4c73b17380e26026687aa5d0d5351ab214","observation_id":"86c2264d-b5ae-4233-9807-90594debccd2","resolution":{"observed_at":"2026-08-04T21:21:40.929513Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.933379Z","title":"Physics- informed neural networks for inverse problems in supersonic flows","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.933379Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:72360fdc2bfc760d9f935064b97abde4cb92d26936376b0ba6b317a4db7ed1f4","observation_id":"d4a71c38-c8a6-48b8-a01b-622d57963318","resolution":{"observed_at":"2026-08-04T21:21:40.933379Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03864","last_updated":"2024-10-31T10:59:05Z","snapshot_observed_at":"2026-08-06T16:37:11.523910Z","submitted_at":"2024-02-06T10:24:36Z","title":"The Challenges of the Nonlinear Regime for Physics-Informed Neural Networks","version":3},"cited_work":{"arxiv_id":"2402.03864","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.03864","snapshot_observed_at":"2026-08-04T21:21:41.566661Z","title":"The Challenges of the Nonlinear Regime for Physics-Informed Neural Networks","venue":"cs.LG","work_id":"89bdf57e-c949-4eaf-83b5-382011c6cc5b","year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.937477Z"},"links":{"cited_paper":"/paper/2402.03864","citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:f86bbe6f6d38c5f126a3f12a87f9f058af521205b3fbad33aeef30e4370b9134","observation_id":"e3434d5f-b8a5-40a6-afe8-3d79693bdd4e","resolution":{"observed_at":"2026-08-04T21:21:41.582198Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.09988","last_updated":"2023-05-08T00:04:06Z","snapshot_observed_at":"2026-07-06T13:54:29.815715Z","submitted_at":"2022-09-20T20:46:07Z","title":"Investigating and Mitigating Failure Modes in Physics-informed Neural Networks (PINNs)","version":3},"cited_work":{"arxiv_id":"2209.09988","doi":null,"metadata_source":"pith","pith_arxiv_id":"2209.09988","snapshot_observed_at":"2026-08-04T21:21:41.532626Z","title":"Investigating and Mitigating Failure Modes in Physics-informed Neural Networks (PINNs)","venue":"cs.LG","work_id":"19598a2a-c8f6-4b3f-839c-8f468812ff85","year":2022},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.942079Z"},"links":{"cited_paper":"/paper/2209.09988","citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:00ec8fa56eecd17638979faef65d68324040670ab891a3c6f0612b0dbb2b6d31","observation_id":"858ce9d2-6223-4768-b348-a962ca2321c3","resolution":{"observed_at":"2026-08-04T21:21:41.543022Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.946296Z","title":"Approximation of Large Stiff Acausal Models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.946296Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:d915fda9fdeb7e4254e96e31f6a8135487c701ecd72b38b035d5c143dfda35c4","observation_id":"4c76edf6-dbee-40fa-a984-94ea85e96dd0","resolution":{"observed_at":"2026-08-04T21:21:40.946296Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.950369Z","title":"Tackling the curse of dimensionality with physics-informed neural networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.950369Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:bff30d2a626806fd7cde8b1c26a22e9a92e7d32985a00222c9bb65a0de7d045a","observation_id":"1a33aa07-da26-499f-b6a0-2ac7c90a3ff0","resolution":{"observed_at":"2026-08-04T21:21:40.950369Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.954486Z","title":"On the importance of the mathematical formulation to get pinns working","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.954486Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:58a2c5149f1ad796f93ded08e0b4b9409d61d4bcdd02348d1390e34814bfb340","observation_id":"48fa9726-9360-49fc-8751-f85f331cef9b","resolution":{"observed_at":"2026-08-04T21:21:40.954486Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:40.958572Z","title":"Locally adaptive ac- tivation functions with slope recovery for deep and physics-informed neural networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.958572Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:4ec52b3eb5b70d2720c27289a41142c91cf036296f868923fd62983056736cea","observation_id":"2020148b-8f0a-439a-8fff-e27523e4f16c","resolution":{"observed_at":"2026-08-04T21:21:40.958572Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.13748","last_updated":"2023-11-27T04:41:51Z","snapshot_observed_at":"2026-08-03T10:02:51.677449Z","submitted_at":"2022-05-27T03:24:31Z","title":"Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.13748","snapshot_observed_at":"2026-08-04T21:21:40.962626Z","title":"Auto-pinn: understanding and optimizing physics-informed neural architecture","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.962626Z"},"links":{"cited_paper":"/paper/2205.13748","citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:620ae906449ee97f2ff6366c5e8761975bcbf7b6c76e2cba5499f3bb51212363","observation_id":"8fbf4031-fe8e-49c5-8b4b-f7e869f33e96","resolution":{"observed_at":"2026-08-04T21:21:40.962626Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.533093Z","title":"Separable physics-informed neural networks","venue":null,"work_id":"e7949199-88db-4d2a-afe6-bbadee7ddad8","year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.967091Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:4a9acb923c07bb93fc32177ad5ddeff2ad6568835618a4c354e32336ade4d228","observation_id":"e11cfda2-f2d6-4160-88c2-1949ebee3cc6","resolution":{"observed_at":"2026-08-04T21:21:44.537728Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.10646","last_updated":"2022-10-19T15:21:05Z","snapshot_observed_at":"2026-07-06T14:07:46.643941Z","submitted_at":"2022-10-19T15:21:05Z","title":"Robust Regression with Highly Corrupted Data via Physics Informed Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.10646","snapshot_observed_at":"2026-08-04T21:21:40.972142Z","title":"Robust regres- sion with highly corrupted data via physics informed neural networks","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.972142Z"},"links":{"cited_paper":"/paper/2210.10646","citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:6826eba8819df33e28210ff2fc5c5f9b1276a34e429fe8b08d2c89b6e9b9a8e3","observation_id":"4199d28f-1205-4373-bc83-117b05661433","resolution":{"observed_at":"2026-08-04T21:21:40.972142Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.519861Z","title":"Physics-informed neural networks with unknown measurement noise","venue":null,"work_id":"73ad144e-4daf-445a-b876-8063aaec38f0","year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.977237Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:7999e642e291df5235702c412e04df838990a7fdc806bd92636c873e44649168","observation_id":"0626189e-c0fc-4d7f-ad6e-b1f79fdd56c1","resolution":{"observed_at":"2026-08-04T21:21:44.524184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.19923","last_updated":"2024-07-11T14:45:50Z","snapshot_observed_at":"2026-07-06T17:52:54.875134Z","submitted_at":"2024-03-29T02:22:22Z","title":"On the Preprocessing of Physics-informed Neural Networks: How to Better Utilize Data in Fluid Mechanics","version":2},"cited_work":{"arxiv_id":"2403.19923","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.19923","snapshot_observed_at":"2026-08-04T21:21:41.467554Z","title":"On the Preprocessing of Physics-informed Neural Networks: How to Better Utilize Data in Fluid Mechanics","venue":"physics.flu-dyn","work_id":"68deb447-aa46-45da-9c2a-291f9830d27a","year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.981920Z"},"links":{"cited_paper":"/paper/2403.19923","citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:7cb8d8a7c23875309e43da543d5127458f41bad7d071e6607c0ae64b9984480d","observation_id":"8cdc67a0-f4c9-47ea-938c-1a592eccfa58","resolution":{"observed_at":"2026-08-04T21:21:41.481470Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.508016Z","title":"Cleaner combustion","venue":null,"work_id":"1da2539c-6ac5-4b6c-872c-8f1a095dd921","year":2013},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.986523Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:aa5914b13dd6769d5dea320ecabdfb2d002da1188a977a7e91df80c44391b88d","observation_id":"eb848fe0-81b5-44ba-bab0-95bca5842bc2","resolution":{"observed_at":"2026-08-04T21:21:44.511701Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.495169Z","title":"What fuel properties enable higher thermal efficiency in spark- ignited engines? Progress in Energy and Combustion Science , 82:100876, 2021","venue":null,"work_id":"79262eda-8428-4c69-a6bb-41884e8cca7c","year":2021},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.991288Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:5bec3e4367451898bf435ea4e926a5c09718688cfb3817cf4f6b1817d8735104","observation_id":"5813fd88-0362-4415-b1ed-1230383214db","resolution":{"observed_at":"2026-08-04T21:21:44.499122Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.482433Z","title":"On the effects of adding syngas to an ammonia-mild combustion regime—a computational study of the reaction zone structure","venue":null,"work_id":"0d8daeec-18d5-4d2f-8271-2482dc6e9ba7","year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.995479Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:7ab232752fd47c4ae3cc651f773aaf2dba40e472b30917e16d1bf97b4d694e29","observation_id":"b1aba833-dc0e-4942-b08d-d9b415c2369d","resolution":{"observed_at":"2026-08-04T21:21:44.486282Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.468962Z","title":"A comprehensive investigation of acoustic power level in a moderate or intense low oxygen dilution in a jet-in-hot-coflow under various working conditions","venue":null,"work_id":"855695d7-918c-484b-8edd-39e2a8a2642c","year":2019},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.999569Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:921fe0ca8d8da6bf4bad03e2fcd4b6b309d98330a3f537c75b940192a2958ef5","observation_id":"234d12eb-8b5e-469d-a9de-f462d3526452","resolution":{"observed_at":"2026-08-04T21:21:44.473104Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.455712Z","title":"On the effects of nh3 addition to a reacting mixture of h2/ch4 under mild combustion regime: Numerical modeling with a modified edc combus- tion model","venue":null,"work_id":"617b6e55-f555-498b-b017-4208223a8528","year":2022},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.003698Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:b07928df51319ced0db447f816aa4af015901ac05253cb23918ad81f0ef35b58","observation_id":"32e609b4-f986-44ad-92a4-075607377e6c","resolution":{"observed_at":"2026-08-04T21:21:44.459617Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.442289Z","title":"Sustainable energy transition for renewable and low carbon grid electricity generation and supply","venue":null,"work_id":"53ce02e0-9b8d-4201-8478-17ba7a90dc07","year":2022},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.008138Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:98dad64f771e1766a3321768f1da0a57cc31bb7327a55f844cf17b3a7195eae6","observation_id":"6251a638-521d-456d-975e-0aca4fb47ce8","resolution":{"observed_at":"2026-08-04T21:21:44.446483Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.00439","last_updated":"2021-01-31T12:15:26Z","snapshot_observed_at":"2026-07-06T10:37:10.518036Z","submitted_at":"2021-01-31T12:15:26Z","title":"Fuels of the Future for Renewable Energy Sources (Ammonia, Biofuels, Hydrogen)","version":1},"cited_work":{"arxiv_id":"2102.00439","doi":null,"metadata_source":"pith","pith_arxiv_id":"2102.00439","snapshot_observed_at":"2026-08-04T21:21:41.435959Z","title":"Fuels of the Future for Renewable Energy Sources (Ammonia, Biofuels, Hydrogen)","venue":"physics.app-ph","work_id":"f73fe760-b3f1-42a3-b871-16be0cf30b4f","year":2021},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.012112Z"},"links":{"cited_paper":"/paper/2102.00439","citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:cb8abdb8b5c105f4ea294e7aeffd9a1057cec522df3f66e3b4d2da2f03253ffc","observation_id":"3008405b-a92c-495e-b48d-5696581e9f0a","resolution":{"observed_at":"2026-08-04T21:21:41.445911Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.429838Z","title":"A review on alternative fuels in future energy system","venue":null,"work_id":"34a4936a-af1d-489d-be7e-9c4a6cd18a98","year":2020},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.016448Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:9b1ddfeb005c3dba3b6ad0d989797c1bfa128eb1506e83ba38c5b137701f9c4b","observation_id":"0a23fef7-6676-4d51-a517-aac9477bfa38","resolution":{"observed_at":"2026-08-04T21:21:44.433947Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.416410Z","title":"Impact of hydrogen on the environment","venue":null,"work_id":"8c97ea5f-a78a-4696-a1ed-e4c80e5e34bf","year":2011},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.020737Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:ee7ac8fe4a348b023c9080652d8055e155e0a766aadc98202ea624dcd7354294","observation_id":"ce1d4df2-1a07-47fb-ac96-6cc1ea254351","resolution":{"observed_at":"2026-08-04T21:21:44.421135Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.404100Z","title":"Hydrogen combustion, production, and applications: A review","venue":null,"work_id":"2d1e36b6-f158-424a-bebd-2dec50cea89b","year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.024850Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:67109a45f67cc6906443c48a8c13677dc7e32e22d0311cddc3bf1a806db185fe","observation_id":"43853430-3483-4d27-8657-368d87fe5f0a","resolution":{"observed_at":"2026-08-04T21:21:44.408043Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.390490Z","title":"Low nox-lpg staged combustion double swirl flames","venue":null,"work_id":"38e1c38a-be7d-4043-bb20-8a7310db1434","year":2019},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.028851Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:9c53185fdbffab43ec69467084cd3040c6d77abe17e9a2326aef631ecd418131","observation_id":"da362f7e-df64-4eb9-95f0-6fa61ccf8c1f","resolution":{"observed_at":"2026-08-04T21:21:44.394777Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.377979Z","title":"On the effects of fractal geometry on reacting and nonreacting flows in a low- swirl burner: A numerical study with large-eddy simulation","venue":null,"work_id":"035a9b7c-f1fd-4054-8174-97ef156b9556","year":2023},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.032930Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:0d5c11ce77efe645e53fc49345b6a5019215261c932e619f4d4fb45f9b6bee80","observation_id":"8a9b1eae-6b7d-4e62-8dd4-e72e6d67dd84","resolution":{"observed_at":"2026-08-04T21:21:44.381878Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.363692Z","title":"Moderate or intense low-oxygen dilution combustion of methane diluted by co2 and n2","venue":null,"work_id":"d8241f9c-fe38-4ded-935a-031454e57923","year":2015},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.036892Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:ce83507db5db090cbb32558daf2ab12ead5c066ca65ceb0a23bb131ea1a49da2","observation_id":"f66f420d-43e5-4161-ad00-6b980e46d711","resolution":{"observed_at":"2026-08-04T21:21:44.368256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.350083Z","title":"Ammonia combustion and emissions in practical applications: a review","venue":null,"work_id":"104e61fd-30a4-437d-91b5-57c51ed2f8aa","year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.041614Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:f7f72cec0d047558b5b3ed22378b4466fcdd978a03f7260d05de24f64a2c4904","observation_id":"7a52536f-59b0-4bcf-ae81-a8598e6bbee9","resolution":{"observed_at":"2026-08-04T21:21:44.353967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.337698Z","title":"Ammonia combustion in furnaces: A review","venue":null,"work_id":"adfab83b-9068-4aeb-8c26-cbdc912ee024","year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.045595Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:efaedbcf968c43dbaf55754eb44198a2c68253c990d70dabd7343efee2eab7bc","observation_id":"71c0a6a8-612f-4772-b53d-634a432c4b67","resolution":{"observed_at":"2026-08-04T21:21:44.341682Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.324326Z","title":"Science and technology of ammonia combustion","venue":null,"work_id":"bcf1278a-c66e-4799-97f0-7406d7cb6e1d","year":2019},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.049647Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:f3879774ab61f908ae1ae69413b385f3ec68614a9b8afbf2c918ce1f8b1171e0","observation_id":"0c0cc619-b560-4ec3-8e34-2ef6216988c5","resolution":{"observed_at":"2026-08-04T21:21:44.328745Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.310953Z","title":"A review on ammonia blends combustion for industrial applications","venue":null,"work_id":"07835bab-931f-407b-961e-180731e53f23","year":2023},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.054045Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:eefcf98c49a13642b852984596e3013abd8f760c92e335c69347bfc299d0a4a9","observation_id":"941e2167-a2dc-43a4-af77-80d712e0f16b","resolution":{"observed_at":"2026-08-04T21:21:44.315274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.298323Z","title":"Green synthetic fuels: Renewable routes for the conversion of non-fossil feedstocks into gaseous fuels and their end uses","venue":null,"work_id":"f5331123-b2c0-474a-9ff3-654b1a9993a2","year":2020},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.058107Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:127bb432d9b6cdbf5a1b961fdc34da28fbbd476ae3209f5a3705f9081f01b2ad","observation_id":"e155564a-5799-4c5f-b5af-0a9d36a91c1a","resolution":{"observed_at":"2026-08-04T21:21:44.302327Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.284957Z","title":"Biofuels an alternative to traditional fossil fuels: A comprehensive review","venue":null,"work_id":"e6885b13-8910-4274-8410-5f9671bfd22b","year":2023},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.062218Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:55212b1094387734ac8c723585cf16adf52badf9c089c773920b9355ec98e44a","observation_id":"68152a00-84a1-429c-bc0b-023eac47a80e","resolution":{"observed_at":"2026-08-04T21:21:44.289067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.270992Z","title":"Biofuels: An alternative to conventional fuel and energy source","venue":null,"work_id":"9dd88c31-a247-4c83-b6c1-eb58d5e60086","year":2022},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.066370Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:b7f61277bac8f9cf880411249aa8e46de33e7d33a950c04c9c75ad3f3b9ac4e6","observation_id":"6e7d7b29-ef45-42e4-832b-e77adb996dc4","resolution":{"observed_at":"2026-08-04T21:21:44.274967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.257588Z","title":"The feasibility of synthetic fuels in renewable energy systems","venue":null,"work_id":"94926058-a3b9-4b9e-867f-99ec425f37b9","year":2013},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.071035Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:226e11990de831361a71cd65e73c8727f5770ce8773a61dcee4649b64dfaef82","observation_id":"3ce84889-fbfb-4ff5-ae73-5876635a392f","resolution":{"observed_at":"2026-08-04T21:21:44.261525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.244825Z","title":"Fossil fuels and alternative fuels","venue":null,"work_id":"bd3b364d-a435-4cb0-abc9-fdc799bfc406","year":2012},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.075489Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:b5eaf3ef59ef4865c387f6dfc78ab2c655c5966ede8bca8e66d6b30616780a42","observation_id":"3c521827-5b61-456a-bfb5-733f76a087da","resolution":{"observed_at":"2026-08-04T21:21:44.248723Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.231462Z","title":"Oxy-fuel combustion technology: current status, applications, and trends","venue":null,"work_id":"ee717a9b-4c14-4159-916b-8c39a81212fd","year":2017},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.079699Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:361b9343ea218996e062c626edb3770bb32f20c46244b0a9ff9aadd7dee5edd3","observation_id":"ded6b25e-347e-4e18-bcfb-9064abb0edeb","resolution":{"observed_at":"2026-08-04T21:21:44.235380Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.218929Z","title":"Oxy-fuel combustion of solid fuels","venue":null,"work_id":"9701e952-8fab-467a-ba45-8bc6226a52b3","year":2010},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.083705Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:ff86ecdf11cb5e0ece4134458419d70d2c33a1d18be40e6dfdd5e37956cbb4f4","observation_id":"19a346db-fc8b-4816-929f-ee48a2bf5b48","resolution":{"observed_at":"2026-08-04T21:21:44.223005Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.205419Z","title":"Oxy-fuel coal combustion—a review of the current state-of-the-art","venue":null,"work_id":"b06869bd-d427-4e9e-8a11-c50db729e847","year":2011},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.087686Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:1faf96b259663a3f52c632b9ad0c1e63c202f3bcea084221598beb4c17a5f3d5","observation_id":"d0c67437-cc45-41cc-97e2-89c8881a72a9","resolution":{"observed_at":"2026-08-04T21:21:44.209539Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.192032Z","title":"Oxyfuel combustion for clean energy applications","venue":null,"work_id":"906a216d-e9e9-444e-bd53-7bdca7f6a775","year":2019},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.091790Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:4cfd298a581d31579d8ee930268ebdc625d233fc7092e73b788eefea6d858298","observation_id":"5b55973b-51f9-43a5-b726-a782f6cf07c8","resolution":{"observed_at":"2026-08-04T21:21:44.195941Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.179239Z","title":"Technological, economic, and emission analysis of the oxy-combustion process","venue":null,"work_id":"03ae9805-2c20-4157-a2c3-b163285f17fc","year":2025},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.096041Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:9578f34bd59bd2e7bda7ece7eb0ab4509b69430c338423b489c314f6fa0989e0","observation_id":"981df9f1-34c0-4ddc-925d-abe6e8b6c6c2","resolution":{"observed_at":"2026-08-04T21:21:44.182948Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.167240Z","title":"Flame stretch effects on partially premixed flames","venue":null,"work_id":"5bc20086-11ab-41c4-937b-fb57e1f228fc","year":2000},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.099993Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:32a6184e1da3722d791fef607b44a8c12aad8519472ecc268e8379ae235807ea","observation_id":"d876b51e-e835-4f6d-a680-025e075176ac","resolution":{"observed_at":"2026-08-04T21:21:44.170947Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.153983Z","title":"Flame dynamics","venue":null,"work_id":"5f38e945-0ac0-472d-8a04-5d2d408fb4d3","year":2009},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.103866Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:557be37c7f997eb920373a2aa9889a9e09ae1ee73f5b12095752a2ec192209cb","observation_id":"f61f56c6-db8f-4906-bb4f-c4582a23a391","resolution":{"observed_at":"2026-08-04T21:21:44.158013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.140966Z","title":"Large eddy simulation of the effects of radiative heat loss on combustion instability prediction","venue":null,"work_id":"5ecb5112-234a-4b5f-9d71-463ff9ddc9cd","year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.108064Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:3a748fc41957f5ac1ff3606ebb284b007cdfe95ee6a1c8703916a38519f98f52","observation_id":"4fe8994c-ab1d-4f6a-ba0c-a722fe45f603","resolution":{"observed_at":"2026-08-04T21:21:44.145049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.115301Z","title":"Turbulent flows","venue":null,"work_id":"55ecd185-2c6d-4a25-9346-4407cc4f6d3f","year":2020},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.116427Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:ebe89b80da9084e97dd70a961778663cbbf8fd8e5aac7738dfe0da96de8c0dac","observation_id":"adfdad1f-c438-428a-8731-2fe0cff8e5d1","resolution":{"observed_at":"2026-08-04T21:21:44.118936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.102546Z","title":"A pinn-deeponet framework for extracting turbulent combustion closure from multiscalar measurements","venue":null,"work_id":"da631fcc-8172-4c35-b6bc-0dea402838b9","year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.120149Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:97ecac47db2a6eb206707ee4f7347a56226bd3b4854fda1d1860193523102deb","observation_id":"3ac2ea68-300d-4692-b739-949dfdd35801","resolution":{"observed_at":"2026-08-04T21:21:44.106725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.090171Z","title":"Learning thermoa- coustic interactions in combustors using a physics-informed neural network","venue":null,"work_id":"51a1100c-2a8d-49d1-8284-dfe571fb0696","year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.124123Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:aca09562eada4afb505cfbfdaae94dc8d15bd24fd7b643ee532ee1b89698d70a","observation_id":"0be61313-e112-415f-9d12-98e375c71835","resolution":{"observed_at":"2026-08-04T21:21:44.094309Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.076833Z","title":"Predicting bifurcation and amplitude death characteristics of thermoacoustic instabilities from pinns-derived van der pol oscillators","venue":null,"work_id":"e9d1063e-133e-4366-967f-f3caa82f3b97","year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.128689Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:0f0fabc0c31889cd0ceece60063d0552acf6910b54b9a6b06a20e22351a76ff5","observation_id":"89cdcc10-20a9-406c-b70b-106c734defd1","resolution":{"observed_at":"2026-08-04T21:21:44.081116Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.05885","last_updated":"2026-08-03T05:46:26Z","snapshot_observed_at":"2026-08-06T23:27:27.443444Z","submitted_at":"2024-08-26T12:49:16Z","title":"Efficient nonlinear flame response modeling for propulsion thermoacoustic analysis using limited numerical data","version":2},"cited_work":{"arxiv_id":"2409.05885","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.05885","snapshot_observed_at":"2026-08-04T21:21:41.407988Z","title":"Efficient nonlinear flame response modeling for propulsion thermoacoustic analysis using limited numerical data","venue":"cs.LG","work_id":"b7af1266-7503-4dc7-8f56-4b3b0b347be0","year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.133228Z"},"links":{"cited_paper":"/paper/2409.05885","citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:567a4695a90d060f2ccdca5a83f5de4a5406f35ee4b6c389ea010868d11ef7fb","observation_id":"329202cd-6458-4fd6-95f1-bb8a049a8739","resolution":{"observed_at":"2026-08-04T21:21:41.417407Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.07441","last_updated":"2023-08-14T20:26:23Z","snapshot_observed_at":"2026-07-06T16:06:10.797909Z","submitted_at":"2023-08-14T20:26:23Z","title":"Physics-Informed Deep Learning to Reduce the Bias in Joint Prediction of Nitrogen Oxides","version":1},"cited_work":{"arxiv_id":"2308.07441","doi":null,"metadata_source":"pith","pith_arxiv_id":"2308.07441","snapshot_observed_at":"2026-08-04T21:21:41.377805Z","title":"Physics-Informed Deep Learning to Reduce the Bias in Joint Prediction of Nitrogen Oxides","venue":"cs.LG","work_id":"c5340330-3581-4225-b6d1-b0d8e23519d4","year":2023},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.137291Z"},"links":{"cited_paper":"/paper/2308.07441","citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:27fa67f3bcaedc6161ca7d83fd4c49881ddea672e7b1f274a65c47d4baa4dcdd","observation_id":"962dc3a4-579c-4e42-b842-1d1ec3f49c1c","resolution":{"observed_at":"2026-08-04T21:21:41.388557Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04716","last_updated":"2024-05-07T23:43:46Z","snapshot_observed_at":"2026-08-05T16:39:09.302281Z","submitted_at":"2024-05-07T23:43:46Z","title":"Physics-based deep learning reveals rising heating demand heightens air pollution in Norwegian cities","version":1},"cited_work":{"arxiv_id":"2405.04716","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.04716","snapshot_observed_at":"2026-08-04T21:21:41.355766Z","title":"Physics-based deep learning reveals rising heating demand heightens air pollution in Norwegian cities","venue":"cs.CY","work_id":"1e6126fa-8740-4ec6-9eb8-fc8a8c53c5ad","year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.141776Z"},"links":{"cited_paper":"/paper/2405.04716","citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:1d9257b3a175e9dd3c1ae18cf7fa4ae49216ef9f18467a72eab501feed861172","observation_id":"15f1c058-3ea1-4a2e-b6fd-8c0ee66ebbeb","resolution":{"observed_at":"2026-08-04T21:21:41.360959Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.063550Z","title":"A physics-informed neural net- work that considers monotonic relationships for predicting nox emissions from coal-fired boilers","venue":null,"work_id":"695ee74c-e94b-4533-9984-55084acd3afa","year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.145942Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:cbb159b3ea86afc7cf104c6515aa484c4106d0e476f4bb94ad024af47f13468f","observation_id":"cf5cfd21-7311-47fc-8fa5-7fd1dd3cc79a","resolution":{"observed_at":"2026-08-04T21:21:44.067547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.051152Z","title":"Reconstructing soot fields in acoustically forced laminar sooting flames using physics-informed machine learn- ing models","venue":null,"work_id":"6c526f57-6c74-4a32-9175-487bf67d2481","year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.149923Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:02513f9cb4e840b09eefa87232fbf7798de8cbe8530027ea38028ef92a5f4ee2","observation_id":"e990a7ca-76e9-4294-b7de-9010bdb213a8","resolution":{"observed_at":"2026-08-04T21:21:44.055140Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.038747Z","title":"Soot temperature and volume fraction field predictions via line-of-sight soot integral radiation equation informed neural networks in laminar sooting flames","venue":null,"work_id":"bfb5ec51-b22c-4687-80e5-5b1f1591f909","year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.153801Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:1942489fa448c10b2d963e4fd0d28b4647f06002ea5a9020e2926eb5d8c44daa","observation_id":"aff9b60b-156d-4c50-9848-3eb53393d276","resolution":{"observed_at":"2026-08-04T21:21:44.042701Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.026421Z","title":"Accelerating the Chemical Kinetics Calculations in Combustion Simulations Using Physics Informed Neural Networks","venue":null,"work_id":"b52f58e8-ab10-4282-a34e-deb4291bbd42","year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.157881Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:72750c8b7cc02ca23f3fc058107f76a94700dfc1e2cce2dca3dbee5a49375c82","observation_id":"31139aaf-ae12-4b9d-9328-6f0dbedac02f","resolution":{"observed_at":"2026-08-04T21:21:44.030362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.013070Z","title":"Co+ oh→ co 2+ h: The relative reaction rate of five co isotopologues","venue":null,"work_id":"212da9e9-b9a8-4617-abe7-ea230da715c1","year":2002},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.161854Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:17cd9054c772367413a264c66753a3fdfb08ae5234f93350318cca2b35a03f33","observation_id":"f3dd1db6-db32-4b9c-87ce-bab95a0ef6de","resolution":{"observed_at":"2026-08-04T21:21:44.017476Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.000266Z","title":"Instability behaviors and suppression of the unsteady autoignited turbulent jet flame in hot coflow","venue":null,"work_id":"78cc0578-1a24-432b-8fc9-1bb1a6beeff3","year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.165956Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:8b29b49f76ece2d6e62cb8c3b99e8936ca970c3c29b7c00debd5fcd4d876dc07","observation_id":"d0feee4c-2ed3-4364-a3f5-1d01707c7a32","resolution":{"observed_at":"2026-08-04T21:21:44.004736Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:43.986723Z","title":"Heat release rate responses in self-excited thermoacoustic instabilities of tangential swirling non-premixed flames","venue":null,"work_id":"606ed8db-66f3-4597-89ba-1364f0dc3ddd","year":2025},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.170041Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:a970475def70bfd40fcdffc142a51e07b19adda795c56fc989870b964a0d148e","observation_id":"c94740f9-6318-4f92-8704-6389ceff99e6","resolution":{"observed_at":"2026-08-04T21:21:43.991648Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:43.974053Z","title":"Combustion driven oscillations in gas turbines","venue":null,"work_id":"1333877a-3e79-4b30-8f7e-9a37b6da1454","year":2003},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.174514Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:0451820b8555414f531cab24cb49acb5a289d7c21f0f9144ed6af3cf954b3f04","observation_id":"ec8c2780-96c4-4417-bb03-04e4adbaf78d","resolution":{"observed_at":"2026-08-04T21:21:43.977869Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:43.961482Z","title":"Machine learning for thermoacoustics","venue":null,"work_id":"85dca1cf-a5bd-42e0-b334-ac577197361b","year":2023},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.178700Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:7f0398bcd31c8e30420f4883e2837ad32fb969c58aa42d7745d05f0c648be6d7","observation_id":"f150dad0-1ec8-45e8-8f3d-551e0e4c07be","resolution":{"observed_at":"2026-08-04T21:21:43.965662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:43.947376Z","title":"Flame dynamics and unsteady heat release rate of self-excited azimuthal modes in an annular combustor","venue":null,"work_id":"94d678c3-19bb-48c8-97bd-40cad1d19d91","year":2014},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.182858Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:9aaf53872e5cb31d51ab01a01620c11379624fa3554dcc7b5d21309feed5ae5d","observation_id":"de217de3-a59e-4f0d-bb6d-0d695877df6c","resolution":{"observed_at":"2026-08-04T21:21:43.952037Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:43.934289Z","title":"Experimental investigation on the route to vortex-acoustic lock-in phenomenon in bluff body stabilized combustors","venue":null,"work_id":"964c4621-9b07-42a0-90b1-d8ca27cd4a42","year":2021},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.187772Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:719d592e5e071a864175e0d6e10151c1e7cccd4ac9690512dc62896f3d9427d3","observation_id":"dbdb6e03-6189-4780-bfc0-e3fd7baf5e5b","resolution":{"observed_at":"2026-08-04T21:21:43.938382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:43.920778Z","title":null,"venue":null,"work_id":"bd52f2b4-67e8-4695-9509-e62b5872e7b9","year":1988},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.191949Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:b54821a0b47b5677beec7949325d741499f290a9b1962b8b3593fc2617d3cb0e","observation_id":"59f5bf49-e056-4a16-964d-6f003fd69344","resolution":{"observed_at":"2026-08-04T21:21:43.924771Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:43.907774Z","title":"Experimental investigation of high- frequency combustion instabilities in liquid rocket engine","venue":null,"work_id":"67545b0b-b254-43f6-a884-d5b585f43fea","year":2008},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.195976Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:aa065e3b4d8caccfdfb920190ec58dd92cc4cf956f5fedbead24f74d2bba4369","observation_id":"bd9d1047-d512-41f2-8854-4b11d81be87d","resolution":{"observed_at":"2026-08-04T21:21:43.911779Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:43.893652Z","title":"Surrogate modeling for bayesian inverse problems based on physics-informed neural networks","venue":null,"work_id":"93acc8cc-8997-4544-93a1-2dcfb9153875","year":2023},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.200063Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:64c64098fb0b6543b521e2cc103b75df7b19570c16f54108d759dcbb09d21bc1","observation_id":"6c80c093-e282-4e94-8c37-145ae87ec81a","resolution":{"observed_at":"2026-08-04T21:21:43.898478Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:43.879659Z","title":"Neural network pid control for combustion instability","venue":null,"work_id":"61a31a5d-b156-4f44-9c23-b3df90a56d9e","year":2022},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.204938Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:8408700778bc3b7c23adcb2742b863b00aeec4ec6dbd9ee635d09b5e009d91c6","observation_id":"492c1bba-5d2c-42c2-943d-c23bf600148b","resolution":{"observed_at":"2026-08-04T21:21:43.883773Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:43.865848Z","title":"Conservative physics- informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems","venue":null,"work_id":"9d9a7cf6-0e22-4348-8803-e8f89d165543","year":2020},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.209620Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:ec206921c71630080f8a823079dd438ba81735f0a4508a22121dd9be9100bf03","observation_id":"aa947542-8a1d-4861-a0b3-60e6e4f636b4","resolution":{"observed_at":"2026-08-04T21:21:43.869800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:43.662412Z","title":"Assimilation of experimental data to create a quantitatively accurate reduced-order thermoacoustic model","venue":null,"work_id":"ccc5bc3c-f50e-46d8-a7ab-5a145e6efd76","year":2021},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.213707Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:b6004405765eb1afec9beff649591c698765bc83a7e343819d25a1602527cea3","observation_id":"aec831e2-6981-49ff-a3a2-5b911dacd3b3","resolution":{"observed_at":"2026-08-04T21:21:43.767127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:43.510928Z","title":"Generating a physics-based quantitatively- accurate model of an electrically-heated rijke tube with bayesian inference","venue":null,"work_id":"c83cd711-734b-4d55-87a5-bc389e4d9308","year":2022},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.218448Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:282e7115e8be40c40eeec8a769a89c70d40443b8eed889109439696b786b5c34","observation_id":"c6632520-3537-4130-bb28-a83ed55d9677","resolution":{"observed_at":"2026-08-04T21:21:43.572238Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:43.347279Z","title":"Premixed ammonia/hydrogen swirl combustion under rich fuel conditions for gas turbines operation","venue":null,"work_id":"e7ca6f52-50bf-427d-99a5-d5e08ea88d27","year":2019},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.222583Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:7dd9c404f27e5449761b0c8b72d1b826fcbd0ec81dd0b1c801801ed8d497c9b7","observation_id":"9e9450dc-15fb-4780-8943-20e60c7b96f0","resolution":{"observed_at":"2026-08-04T21:21:43.425797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:43.232546Z","title":"A physics-informed neural network based simulation tool for reacting flow with multicomponent reactants","venue":null,"work_id":"4db62051-3a18-4ec6-ab6a-4e0982a74e03","year":2023},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.226739Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:d2fb31845f26a37ba1693151273d54d9608a1f39c3854da2bc9e60caef924dd0","observation_id":"c9ecf042-370a-43a3-b187-5e6c1e23e5be","resolution":{"observed_at":"2026-08-04T21:21:43.282271Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:43.113322Z","title":"The application of physics-informed machine learning in multiphysics modeling in chemical engineering","venue":null,"work_id":"7dd6df13-d269-48a6-aece-af140432a0bb","year":2023},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.230847Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:9023658e2ebb159096b8849a32133946a301cb6351afd6d1b889f4f26e2a9387","observation_id":"0580eb1d-1048-4479-8866-33f19a54ad2e","resolution":{"observed_at":"2026-08-04T21:21:43.190876Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:42.976825Z","title":"Phelan, and Paolo Barucca","venue":null,"work_id":"4418ea0b-c6f8-4660-9b7e-35e25241acee","year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.235050Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:e4cbe3bdf0a1872d13b61fc3d0c68ea404959c2db37af08c07d94bb53caa08e4","observation_id":"f9e3faed-62db-4f95-9181-95832dd1b112","resolution":{"observed_at":"2026-08-04T21:21:43.039456Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:42.801533Z","title":"Mpc-guided, data-driven fuzzy controller synthesis","venue":null,"work_id":"551d5b7a-d918-4284-aae9-303e1fcf7198","year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.238862Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:2c4603d96575191532a073f3f08ddd357131d739b5c9ffa2d6c0505177e5efdc","observation_id":"30dce071-f130-4088-b8c0-3a58186606fb","resolution":{"observed_at":"2026-08-04T21:21:42.881434Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:42.651289Z","title":"From pinns to pikans: Re- cent advances in physics-informed machine learning","venue":null,"work_id":"a2b06119-53d9-47d1-8990-62b6b3802d73","year":2025},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.243039Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:1bb762c074004e74f20131e4f49630eb66782de6949e9ae0622626b27a6139a5","observation_id":"39ff1c24-f51c-4f39-a010-038a7f04a586","resolution":{"observed_at":"2026-08-04T21:21:42.728414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T21:21:44.127447Z","title":"Physics-informed neural networks coupled with flamelet/progress variable model for solv- ing combustion physics considering detailed reaction mechanism","venue":null,"work_id":"b4332994-4ce3-4a69-aeea-5a7c5eb89849","year":2024},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.247048Z"},"links":{"citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:e6978ae212a62bed3f439d2a42e55fa46811fc9bf32d1509e43b84f57559dcf0","observation_id":"0a785a8e-cc19-4345-8756-bc83f9fc2cef","resolution":{"observed_at":"2026-08-04T21:21:44.131697Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2208.12045","last_updated":"2022-08-23T09:20:42Z","snapshot_observed_at":"2026-08-06T06:04:41.922386Z","submitted_at":"2022-08-23T09:20:42Z","title":"Reduced-PINN: An Integration-Based Physics-Informed Neural Networks for Stiff ODEs","version":1},"cited_work":{"arxiv_id":"2208.12045","doi":null,"metadata_source":"pith","pith_arxiv_id":"2208.12045","snapshot_observed_at":"2026-08-04T21:21:41.333516Z","title":"Reduced-PINN: An Integration-Based Physics-Informed Neural Networks for Stiff ODEs","venue":"cs.LG","work_id":"feb599f9-617a-44b3-b12d-2a6bd5fc81a6","year":2022},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.250911Z"},"links":{"cited_paper":"/paper/2208.12045","citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:8953bce02bf09f2996683bdfa2529f68620364bd06631d5af2efdf4391bd6c1e","observation_id":"7fa4f484-ae4c-4608-8770-eff641b85874","resolution":{"observed_at":"2026-08-04T21:21:41.340020Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","latest_version":1,"primary_category":"physics.flu-dyn","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":31,"verified_exact":10,"verified_fuzzy":59},"total_outbound_references":108},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 100 of 108 outbound references and 0 inbound Pith citation observations for arXiv:2509.08094."}