{"as_of":"2026-08-21T18:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:20c3563fb024fa41b8754290fc59a5a6d16cfc76481f78519e202db8d2d246d3","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":11,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":11,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":11,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T12:31:31.017458Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-18T09:06:09.254718Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2107.01272","last_updated":"2023-02-28T21:42:55Z","snapshot_observed_at":"2026-08-21T03:54:18.953487Z","submitted_at":"2021-07-02T20:59:03Z","title":"Physics-Guided Deep Learning for Dynamical Systems: A Survey","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.01272","snapshot_observed_at":"2026-08-16T12:31:31.017458Z","title":"Wang and R","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.12675","last_updated":"2025-04-22T05:57:01Z","snapshot_observed_at":"2026-08-16T12:23:09.352360Z","submitted_at":"2025-04-17T06:06:53Z","title":"Physics Informed Constrained Learning of Dynamics from Static Data","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-16T12:31:31.017458Z"},"links":{"cited_paper":"/paper/2107.01272","citing_paper":"/paper/2504.12675"},"observation_digest":"sha256:5085e6627182c76403f2ac23d190fd79384781bcde7fe452d1dd5cbe9e5b6f1c","observation_id":"584ccf98-9e51-4390-8ce3-a012936dca00","resolution":{"observed_at":"2026-08-16T12:31:31.017458Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.01272","last_updated":"2023-02-28T21:42:55Z","snapshot_observed_at":"2026-08-21T03:54:18.953487Z","submitted_at":"2021-07-02T20:59:03Z","title":"Physics-Guided Deep Learning for Dynamical Systems: A Survey","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.01272","snapshot_observed_at":"2026-08-16T11:11:41.763911Z","title":"Physics-guided deep learning for dynamical systems: A survey,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.16383","last_updated":"2025-04-23T03:24:57Z","snapshot_observed_at":"2026-08-19T08:38:55.928072Z","submitted_at":"2025-04-23T03:24:57Z","title":"Fast and Modular Whole-Body Lagrangian Dynamics of Legged Robots with Changing Morphology","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-16T11:11:41.763911Z"},"links":{"cited_paper":"/paper/2107.01272","citing_paper":"/paper/2504.16383"},"observation_digest":"sha256:5dc0492e655ba06b82876c9dd34f0cd31e42d8308ff2a672bc486d063c5e8d43","observation_id":"48ea0533-b1d2-47b3-9190-940228da56f7","resolution":{"observed_at":"2026-08-16T11:11:41.763911Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.01272","last_updated":"2023-02-28T21:42:55Z","snapshot_observed_at":"2026-08-21T03:54:18.953487Z","submitted_at":"2021-07-02T20:59:03Z","title":"Physics-Guided Deep Learning for Dynamical Systems: A Survey","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.01272","snapshot_observed_at":"2026-08-15T20:28:18.619584Z","title":"Physics-guided deep learning for dynamical systems: A survey.arXiv preprint arXiv:2107.01272, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.12882","last_updated":"2025-05-19T09:10:55Z","snapshot_observed_at":"2026-08-21T15:23:27.920109Z","submitted_at":"2025-05-19T09:10:55Z","title":"PhyDA: Physics-Guided Diffusion Models for Data Assimilation in Atmospheric Systems","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T20:28:18.619584Z"},"links":{"cited_paper":"/paper/2107.01272","citing_paper":"/paper/2505.12882"},"observation_digest":"sha256:f8e48583fc68666b744d6165a48d9c44df063e23c37843699327e93dc224d57f","observation_id":"9c7e31d6-bc47-490b-9e0d-1287e7b55b5c","resolution":{"observed_at":"2026-08-15T20:28:18.619584Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.01272","last_updated":"2023-02-28T21:42:55Z","snapshot_observed_at":"2026-08-21T03:54:18.953487Z","submitted_at":"2021-07-02T20:59:03Z","title":"Physics-Guided Deep Learning for Dynamical Systems: A Survey","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.01272","snapshot_observed_at":"2026-08-15T20:28:21.938435Z","title":"Physics-guided deep learning for dynam- ical systems: A survey,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13042","last_updated":"2025-05-19T12:32:28Z","snapshot_observed_at":"2026-08-20T11:49:33.377488Z","submitted_at":"2025-05-19T12:32:28Z","title":"An introduction to Neural Networks for Physicists","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T20:28:21.938435Z"},"links":{"cited_paper":"/paper/2107.01272","citing_paper":"/paper/2505.13042"},"observation_digest":"sha256:8d0ee515bdcd37afc54a8957a56533b05fa3d3040b78b9c4c886f513615390fe","observation_id":"bcb64a9f-3d2a-4597-9e81-808e78bfb7ab","resolution":{"observed_at":"2026-08-15T20:28:21.938435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.01272","last_updated":"2023-02-28T21:42:55Z","snapshot_observed_at":"2026-08-21T03:54:18.953487Z","submitted_at":"2021-07-02T20:59:03Z","title":"Physics-Guided Deep Learning for Dynamical Systems: A Survey","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.01272","snapshot_observed_at":"2026-08-07T13:03:43.309926Z","title":"Physics-Guided Deep Learning for Dynamical Systems: A Survey,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.22890","last_updated":"2025-05-28T21:46:19Z","snapshot_observed_at":"2026-08-18T22:44:30.688268Z","submitted_at":"2025-05-28T21:46:19Z","title":"Physics-Infused Reduced-Order Modeling for Analysis of Multi-Layered Hypersonic Thermal Protection Systems","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T13:03:43.309926Z"},"links":{"cited_paper":"/paper/2107.01272","citing_paper":"/paper/2505.22890"},"observation_digest":"sha256:0bf78138f298d8d081293ec7ce5d86e4f434d12d742aafa5861ddc366e109739","observation_id":"6bbcdd76-84ec-412e-a687-7d016a724364","resolution":{"observed_at":"2026-08-07T13:03:43.309926Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.01272","last_updated":"2023-02-28T21:42:55Z","snapshot_observed_at":"2026-08-21T03:54:18.953487Z","submitted_at":"2021-07-02T20:59:03Z","title":"Physics-Guided Deep Learning for Dynamical Systems: A Survey","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.01272","snapshot_observed_at":"2026-08-06T22:52:50.952931Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.20771","last_updated":"2026-06-27T01:54:59Z","snapshot_observed_at":"2026-08-20T11:17:45.371901Z","submitted_at":"2025-06-25T19:04:02Z","title":"Stochastic and Non-local Closure Modeling for Nonlinear Dynamical Systems via Latent Score-based Generative Models","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T22:52:50.952931Z"},"links":{"cited_paper":"/paper/2107.01272","citing_paper":"/paper/2506.20771"},"observation_digest":"sha256:dbbc0b4b8a9661bcb9250c161bd4a1730ee3e4c2ece45cece3e5eabc94040e6c","observation_id":"81ac4c23-ca8c-4da8-85bc-b92193f54788","resolution":{"observed_at":"2026-08-06T22:52:50.952931Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.01272","last_updated":"2023-02-28T21:42:55Z","snapshot_observed_at":"2026-08-21T03:54:18.953487Z","submitted_at":"2021-07-02T20:59:03Z","title":"Physics-Guided Deep Learning for Dynamical Systems: A Survey","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.01272","snapshot_observed_at":"2026-08-07T00:26:24.780349Z","title":"Physics-guided deep learning for dynamical systems: A survey,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.00025","last_updated":"2025-06-17T09:11:34Z","snapshot_observed_at":"2026-08-10T08:33:14.654391Z","submitted_at":"2025-06-17T09:11:34Z","title":"Generalizing to New Dynamical Systems via Frequency Domain Adaptation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T00:26:24.780349Z"},"links":{"cited_paper":"/paper/2107.01272","citing_paper":"/paper/2507.00025"},"observation_digest":"sha256:94981f8ff49daac0e2d8f022ca83f347c92f381ec9209cdfa5667ab33d33cd0d","observation_id":"8169a715-0b4d-40c7-b7c0-d94089122add","resolution":{"observed_at":"2026-08-07T00:26:24.780349Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.01272","last_updated":"2023-02-28T21:42:55Z","snapshot_observed_at":"2026-08-21T03:54:18.953487Z","submitted_at":"2021-07-02T20:59:03Z","title":"Physics-Guided Deep Learning for Dynamical Systems: A Survey","version":6},"cited_work":{"arxiv_id":"2107.01272","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2107.01272","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Rui Wang and Rose Yu","venue":null,"work_id":"bdb243b9-d8ed-4f22-ae92-2471108566e2","year":null},"citing_paper":{"arxiv_id":"2510.06637","last_updated":"2026-05-10T00:27:22Z","snapshot_observed_at":"2026-08-11T10:49:31.596318Z","submitted_at":"2025-10-08T04:37:32Z","title":"Control-Augmented Autoregressive Diffusion for Data Assimilation","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-18T09:02:38.416697Z"},"links":{"cited_paper":"/paper/2107.01272","citing_paper":"/paper/2510.06637"},"observation_digest":"sha256:20f71ae14012341d7819f33f1fedc2633a691a4115c205bb81c875e97c52d751","observation_id":"0125b500-bc48-4b17-96b2-bfa23239317e","resolution":{"observed_at":"2026-05-18T09:06:09.256856Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.01272","last_updated":"2023-02-28T21:42:55Z","snapshot_observed_at":"2026-08-21T03:54:18.953487Z","submitted_at":"2021-07-02T20:59:03Z","title":"Physics-Guided Deep Learning for Dynamical Systems: A Survey","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.01272","snapshot_observed_at":"2026-08-04T09:41:14.038676Z","title":"Physics-guided deep learning for dynamical systems: A survey","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.14007","last_updated":"2026-07-03T15:25:33Z","snapshot_observed_at":"2026-08-20T06:56:24.214258Z","submitted_at":"2025-10-15T18:38:36Z","title":"Conditional Clifford-Steerable CNNs for PDE Modeling","version":2},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-04T09:41:14.038676Z"},"links":{"cited_paper":"/paper/2107.01272","citing_paper":"/paper/2510.14007"},"observation_digest":"sha256:b5c7ce695cda8871f764e8f0a9cb89d6d337c65260978e37e90667c1f5918c79","observation_id":"2e441fc9-2fd7-44b7-84d7-90bbe1bc9b58","resolution":{"observed_at":"2026-08-04T09:41:14.038676Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.01272","last_updated":"2023-02-28T21:42:55Z","snapshot_observed_at":"2026-08-21T03:54:18.953487Z","submitted_at":"2021-07-02T20:59:03Z","title":"Physics-Guided Deep Learning for Dynamical Systems: A Survey","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.01272","snapshot_observed_at":"2026-08-03T18:15:38.912370Z","title":"First ed., Kindle Direct Publishing","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2512.06315","last_updated":"2026-07-15T05:45:11Z","snapshot_observed_at":"2026-08-21T03:26:59.824479Z","submitted_at":"2025-12-06T06:18:49Z","title":"Control-Oriented System Identification: Classical, Learning, and Physics-Informed Approaches","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-03T18:15:38.912370Z"},"links":{"cited_paper":"/paper/2107.01272","citing_paper":"/paper/2512.06315"},"observation_digest":"sha256:670da0f17eb7ab3298dbd3e3f10a3e4ae3c9fd0015672dd988b4244798ec2c45","observation_id":"f4119cee-a65c-459e-a764-8dc07d4bbf66","resolution":{"observed_at":"2026-08-03T18:15:38.912370Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.01272","last_updated":"2023-02-28T21:42:55Z","snapshot_observed_at":"2026-08-21T03:54:18.953487Z","submitted_at":"2021-07-02T20:59:03Z","title":"Physics-Guided Deep Learning for Dynamical Systems: A Survey","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.01272","snapshot_observed_at":"2026-08-05T00:46:52.241766Z","title":"arXiv preprint arXiv:2107.01272 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.01582","last_updated":"2026-08-03T01:34:13Z","snapshot_observed_at":"2026-08-20T16:22:05.049614Z","submitted_at":"2026-08-03T01:34:13Z","title":"LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-05T00:46:52.241766Z"},"links":{"cited_paper":"/paper/2107.01272","citing_paper":"/paper/2608.01582"},"observation_digest":"sha256:2d77de3fc83044c97885ae87fc6e5377f23334375bccbeec57418a36203b917b","observation_id":"ab4d78de-0dc7-4a59-81d5-98c69ba97d33","resolution":{"observed_at":"2026-08-05T00:46:52.241766Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2107.01272/citation-record","integrity":"/paper/2107.01272/integrity","json":"/paper/2107.01272/citation-record.json","paper":"/paper/2107.01272"},"outbound":[],"paper":{"arxiv_id":"2107.01272","last_updated":"2023-02-28T21:42:55Z","latest_version":6,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-21T03:54:18.953487Z","submitted_at":"2021-07-02T20:59:03Z","title":"Physics-Guided Deep Learning for Dynamical Systems: A Survey"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2107.01272."}