{"as_of":"2026-08-07T08:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d5319cb03634c0d15e6b140fcf353c71f0672c6b4be10083fc1e9b99c826d6a3","coverage":[{"denominator":88,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":88,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T12:32:12.306068Z","state":"measured"},{"denominator":89,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":89,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T08:43:21.966943Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.21684","snapshot_observed_at":"2026-08-03T08:43:21.966943Z","title":"Andrew Witkin and Michael Kass","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.16096","last_updated":"2026-06-24T05:09:55Z","snapshot_observed_at":"2026-08-07T03:34:28.999555Z","submitted_at":"2026-01-22T16:46:28Z","title":"Neural Particle Automata: Learning Self-Organizing Particle Dynamics","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-03T08:43:21.966943Z"},"links":{"cited_paper":"/paper/2507.21684","citing_paper":"/paper/2601.16096"},"observation_digest":"sha256:85fe6f1ab737ee3a1641c696c1e276282d5e06c05bddd79e03c2141dd71d929a","observation_id":"158c5e41-58a7-46f6-8f2c-b28b312112ab","resolution":{"observed_at":"2026-08-03T08:43:21.966943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2507.21684/citation-record","integrity":"/paper/2507.21684/integrity","json":"/paper/2507.21684/citation-record.json","paper":"/paper/2507.21684"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:32:11.614901Z","title":"Smoothed particle hydrodynamics: theory and application to non-spherical stars","venue":null,"work_id":null,"year":1977},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.614901Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:a7f40870cf5a87038360cf78cedf046407e9319448a5d0b5795873c131d075cd","observation_id":"c16701d6-9e29-456b-a76a-ec9da3f0d088","resolution":{"observed_at":"2026-08-06T12:32:11.614901Z","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":"2026-08-06T12:32:11.622243Z","title":"Smoothed particle hydrodynamics in astrophysics","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.622243Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:42c696a5bb67050dba07f13acb43f591de914fdcaa9c9bade13d699c57c0d646","observation_id":"cea6c571-d083-485f-ab88-3546330de404","resolution":{"observed_at":"2026-08-06T12:32:11.622243Z","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":"2026-08-06T12:32:11.628772Z","title":"Smoothed particle hydrodynamics (sph) for complex fluid flows: Recent developments in methodology and applications","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.628772Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:14280061ba3a60007cb306e9ff15a514f49cf6d2b5e9b7b58e05ac96e5e87123","observation_id":"798ec7a9-9fc3-4cba-8c6d-9efb54f5d9f9","resolution":{"observed_at":"2026-08-06T12:32:11.628772Z","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":"2026-08-06T12:32:11.636964Z","title":"A survey on sph methods in computer graphics","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.636964Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:721634be53feba90a3c0f74e4d62aa73a6d5fa12f8f98ac4e7949f683ef2c29e","observation_id":"19f4d442-c112-485e-a2d8-9104b7df30cc","resolution":{"observed_at":"2026-08-06T12:32:11.636964Z","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":"2026-08-06T12:32:11.643920Z","title":"Rogers, and Antonio Souto-Iglesias","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.643920Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:b3ac2f263404699541138e4a7c97d78d57b9bb75b49f155f5ce1f75c10afc50b","observation_id":"81e924ea-1ce1-47fc-af14-7d9ca6a8b41f","resolution":{"observed_at":"2026-08-06T12:32:11.643920Z","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":"2026-08-06T12:32:11.651614Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.651614Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:13c2e066bdb890b15675d6f4364be4fff7d8d4a32dea5761159c26f12c4e3820","observation_id":"e7dec238-2a51-43a8-a9b6-29a9dd0bdff5","resolution":{"observed_at":"2026-08-06T12:32:11.651614Z","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":"2026-08-06T12:32:11.658991Z","title":"Improving language under- standing by generative pre-training","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.658991Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:7631553ccb72bf583e4ca0d8a2246be66a169a9133bcba3fb9b42e1a6670f15d","observation_id":"6c4ce533-cc13-455b-9c3b-3b73af8f3556","resolution":{"observed_at":"2026-08-06T12:32:11.658991Z","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":"2026-08-06T12:32:11.666836Z","title":"Mastering the game of go without human knowledge","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.666836Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:ad2e23ed832bc86e3b87c8205c23f40a73ec43e8f29f431cf8816ea3713a63a5","observation_id":"756c2b10-af37-4046-9294-f96b05856530","resolution":{"observed_at":"2026-08-06T12:32:11.666836Z","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":"2026-08-06T12:32:11.674276Z","title":"Deep learning, volume 1","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.674276Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:9b6ef7d4c8e8515e7775520b702835c21e2027998710d8385b7eebec4b115eff","observation_id":"d3808161-8623-436c-990c-ae2e7338db76","resolution":{"observed_at":"2026-08-06T12:32:11.674276Z","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":"2026-08-06T12:32:13.888921Z","title":"Highly accurate protein structure prediction with alphafold","venue":null,"work_id":"e87aa847-1754-4251-a464-c3d741b0f34e","year":2021},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.681068Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:c399be210b14e8d62453b8edf2195699ece6b03b37039ca8f9933b016ae227a8","observation_id":"7fbb1794-7d31-4266-8402-62531535198a","resolution":{"observed_at":"2026-08-06T12:32:13.894835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.868845Z","title":"phiflow: A differentiable pde solving framework for deep learning via physical simulations","venue":null,"work_id":"04ecba87-57f9-445d-902c-1e2db0c69be6","year":2020},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.697988Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:0a6e00a1ad5b900efc4bac47aa459c27a134ccbb697d21989f7b11e5689fab37","observation_id":"af11c243-00a0-4087-8dc8-3dfe889d8723","resolution":{"observed_at":"2026-08-06T12:32:13.875155Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.00935","last_updated":"2020-02-14T06:21:07Z","snapshot_observed_at":"2026-08-04T18:08:10.197355Z","submitted_at":"2019-10-01T05:00:26Z","title":"DiffTaichi: Differentiable Programming for Physical Simulation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.00935","snapshot_observed_at":"2026-08-06T12:32:11.704212Z","title":"Difftaichi: Differentiable programming for physical simulation","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.704212Z"},"links":{"cited_paper":"/paper/1910.00935","citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:7a93c94a4a0dc1bba628100a9adabedde8a7361964d67875d36783dd2e1f8fdf","observation_id":"f6a6f9a3-bbec-48f1-9796-1dba129eabb6","resolution":{"observed_at":"2026-08-06T12:32:11.704212Z","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":"2026-08-06T12:32:13.844647Z","title":"Apebench: A benchmark for autoregressive neural emulators of pdes","venue":null,"work_id":"fe286682-29a2-487a-a191-673785008c9b","year":2024},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.712427Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:e504f5def9ea23d003e7f7e4142c2539fa28a263b83c46002827c1ebdc07545c","observation_id":"7cf59076-6557-4b82-87b9-57eb0fa7a954","resolution":{"observed_at":"2026-08-06T12:32:13.854467Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.822158Z","title":"Pytorch: An imperative style, high-performance deep learning library, 2019","venue":null,"work_id":"c7370a17-91a4-4dd5-9b61-905b74aa3565","year":2019},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.719585Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:70c9fff1269c89b1e23e83ff3d4bce05bc4049673ca31ff06a72506ef6e5ce67","observation_id":"7acece1f-c2de-43ac-87eb-037e9833997c","resolution":{"observed_at":"2026-08-06T12:32:13.831092Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:11.727007Z","title":"JAX: composable transformations of Python+NumPy programs, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.727007Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:7866829d71e85c843d2cb0e353fc2b7785307c8ab4b659f5630ebce2191f2c78","observation_id":"9f133d37-5c2e-4f72-9010-a0818a459dc5","resolution":{"observed_at":"2026-08-06T12:32:11.727007Z","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":"2026-08-06T12:32:13.791051Z","title":"Universal physics transformers","venue":null,"work_id":"cee67f73-8d9a-4bfc-adbe-4efe3735f6ca","year":2024},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.733454Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:8e55daab35e3038813e236f40f54a455cd7b26752b4565923d1f9d22c3498f16","observation_id":"2557996f-fe7e-476c-9afb-50f98ca23f70","resolution":{"observed_at":"2026-08-06T12:32:13.796826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.773985Z","title":"Symmetric basis convolutions for learning lagrangian fluid me- chanics","venue":null,"work_id":"10f39d74-b75b-4706-adcd-34a1a52a4b04","year":2024},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.739743Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:72b723c76c720f26d5f060249ed45464a82ef49a0ea354e9210da401d9c0528f","observation_id":"249a47f2-0ac4-4c20-bd3e-ff423164005e","resolution":{"observed_at":"2026-08-06T12:32:13.779647Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.756640Z","title":"Physics- informed neural networks (pinns) for fluid mechanics: A review, 2021","venue":null,"work_id":"f3c72deb-b6e7-491a-bf92-25a05d8519e7","year":2021},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.746202Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:3391b26018d9fccae1e324265e6cefe1da2df881fa03a612c910de04e07b74e6","observation_id":"deca4f29-149c-4d32-8c2d-6159281ec021","resolution":{"observed_at":"2026-08-06T12:32:13.762531Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.740201Z","title":"Solver-in-the-loop: Learning from differentiable physics to interact with iterative pde-solvers","venue":null,"work_id":"03bee05c-e040-4c4a-ad1a-bcd7918e2cae","year":2020},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.753690Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:29231447b0e9f0f4a14139217a8d4030e176b1521e2957faea7a3bb13aad2539","observation_id":"f941f2b2-7ff8-46d9-bb35-fbf8a370a795","resolution":{"observed_at":"2026-08-06T12:32:13.745093Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.722581Z","title":"Adjoint sys- tem method in shape optimization of some typical fluid flow patterns","venue":null,"work_id":"00123853-a79b-454b-80ee-33f795917957","year":2019},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.759297Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:2c9c19a7c7550b44307dad39600bcc0495a157392e3f625c30b8e22eededf314","observation_id":"fd62d3df-b7f3-4f7f-a99f-b6daa3b9e949","resolution":{"observed_at":"2026-08-06T12:32:13.728106Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.704638Z","title":"Deep learning methods for reynolds-averaged navier–stokes simulations of airfoil flows","venue":null,"work_id":"3f9390fb-156a-4b1a-9bc2-5200e9e4790f","year":2020},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.766744Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:a599264dc7640bee2dba904da2a42403d748d90c8cf9279d64abfeccbe4dd339","observation_id":"4425eba9-a17f-4b39-8d47-08a25ba6e854","resolution":{"observed_at":"2026-08-06T12:32:13.711228Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.688140Z","title":"Simulating cosmic structure formation with the gadget-4 code","venue":null,"work_id":"260a30c2-3789-48c6-a4f1-f2935246ef10","year":2021},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.775567Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:d1a3e1eeeaa8fc1179444aa7420049b799ddeaa64f291f3cf41f90eff64b3c64","observation_id":"da5d22cc-d11d-4d17-869b-014acd2e6403","resolution":{"observed_at":"2026-08-06T12:32:13.693532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.671395Z","title":"A new class of accurate, mesh-free hydrodynamic simulation methods","venue":null,"work_id":"847ced40-f3e6-4e18-bcab-55fb6c375036","year":2015},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.784355Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:30bbd594da5af8328d74cb82b524fe0d3a0a0dfe7ab946221afe9d1b353b1f70","observation_id":"cb29c91b-7a1e-48be-87f6-9792488cae91","resolution":{"observed_at":"2026-08-06T12:32:13.676415Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.652464Z","title":"Swift: Sph with inter-dependent fine-grained tasking.Astrophysics source code library, pages ascl–1805, 2018","venue":null,"work_id":"b63448c6-af12-4a31-b22d-700f51c8ab1b","year":2018},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.790777Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:569c219ec8bdcde6c620e515ac30399f6821456be1fa9ce794dc21b9cfc357a0","observation_id":"6010449b-ee5b-4f0d-bf4b-9fdbcb8a103b","resolution":{"observed_at":"2026-08-06T12:32:13.658433Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.633598Z","title":"A smoothed particle hydrodynamics mini-app for exascale","venue":null,"work_id":"24e537b8-3788-444e-804d-d1eae749944c","year":2020},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.798996Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:72a18d0de7d3d3aa52439deeda2b2323cc9a61c0888f0ccc80b905e65550c000","observation_id":"31f7de5d-6730-4360-8ae4-8a70b25e58b0","resolution":{"observed_at":"2026-08-06T12:32:13.639638Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.615396Z","title":"Dualsphysics: from fluid dynamics to multiphysics problems","venue":null,"work_id":"6bd2883b-688f-471f-b26d-dca517ebe5f6","year":2022},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.805723Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:a64259cb439d1edf5a15113b615a9179adbbf743bba1c356463e971592ed6890","observation_id":"071e3186-49c2-434c-86ec-2679877bcc4c","resolution":{"observed_at":"2026-08-06T12:32:13.620731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.598862Z","title":"Sphinxsys: An open-source multi-physics and multi-resolution library based on smoothed particle hydrodynamics","venue":null,"work_id":"c8723a89-34e7-4877-aec6-97ca2c5b58d8","year":2021},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.812284Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:d20ab553ffe9db1e56df9994f518a51a1c9d5b0f5666e2b1a92e84f308beb094","observation_id":"34162053-aa98-480e-9a78-e1a41bdd2f58","resolution":{"observed_at":"2026-08-06T12:32:13.603974Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.579715Z","title":"Dinesh, Dileep Menon, Rahul Govind, Suraj Sanka, Amal S","venue":null,"work_id":"02affdd1-d0d6-4dcb-9cf6-a95fb85d4a3b","year":2021},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.819587Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:5e0189a4785a93fd73ace61ff3a72fbe7382636c399f09b270ccaf3b87b2e3f6","observation_id":"6dacb5c9-77a3-48b1-95d9-2a595a9a22e1","resolution":{"observed_at":"2026-08-06T12:32:13.585021Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.561356Z","title":"SPlisHSPlasH Library","venue":null,"work_id":"7ba35b9a-0589-424a-bd18-82fedebf490a","year":null},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.826143Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:8b96c9e70b4d66517703bd3760c368954d0fd1f433890d8f0ff621d0358d0a71","observation_id":"35f7fb0e-13bc-46e6-aa05-88f460cf0f2b","resolution":{"observed_at":"2026-08-06T12:32:13.567279Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.04750","last_updated":"2024-07-07T17:53:28Z","snapshot_observed_at":"2026-08-06T22:00:25.304345Z","submitted_at":"2024-03-07T18:53:53Z","title":"JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.04750","snapshot_observed_at":"2026-08-06T12:32:11.835303Z","title":"Jax-sph: A differentiable smoothed particle hydrodynamics framework","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.835303Z"},"links":{"cited_paper":"/paper/2403.04750","citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:f03583230acd18c58b11c2267312cdb32ab271a3237869bcf38f7d51a25c90f7","observation_id":"11c149cc-edde-4384-9ad5-002baca39de7","resolution":{"observed_at":"2026-08-06T12:32:11.835303Z","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":"2026-08-06T12:32:13.538975Z","title":"Difffr: Differentiable sph-based fluid-rigid coupling for rigid body control","venue":null,"work_id":"e19c899b-cb13-46eb-a0df-2232f11446d6","year":2023},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.842837Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:4f4c0900a7c5f956c3e4ec0a56142feddfe29517af58b8143c469ccfa759bfb9","observation_id":"71796228-91f8-4943-98b4-a41b129c4781","resolution":{"observed_at":"2026-08-06T12:32:13.547538Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.518911Z","title":"Warp: A high-performance python framework for gpu simulation and graphics","venue":null,"work_id":"20d50628-a761-4b44-8b2a-a3c5c6e46e24","year":2022},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.850537Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:08bdbd957f4230a39ae4caafa1dae2d363d330ef4f23351bc773393ad38f820e","observation_id":"08f4005d-9bbe-4093-8c02-e63a5dec5186","resolution":{"observed_at":"2026-08-06T12:32:13.525390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.502484Z","title":"Lagrangebench: A lagrangian fluid mechanics benchmarking suite","venue":null,"work_id":"bf840d8d-997e-4726-a29e-f859a5f4a5c6","year":2023},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.857524Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:4e8935410b2f4760d89de342b7801e05d8b1cb5e18615bd280419911e64d4330","observation_id":"9062bffd-52aa-49ae-b5ba-f36c52f27c61","resolution":{"observed_at":"2026-08-06T12:32:13.507680Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.484176Z","title":"Smoothed particle hydrodynamics and magnetohydrodynamics","venue":null,"work_id":"4c76069d-e2b0-4bb6-a7ce-441246687a04","year":2012},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.864731Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:f1ed8e70077a954e4af16ee8d31a6eb80c1f8a91a0a9a57ec300057fbcea9ad3","observation_id":"05a4b1ae-2e0f-48b3-b994-c890902fbcd6","resolution":{"observed_at":"2026-08-06T12:32:13.489994Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.463640Z","title":"Smoothed particle hydrodynamics","venue":null,"work_id":"48d74e53-ec12-4e8d-91e4-eb42ef6ad535","year":2005},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.875233Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:98c06e4515eb95e0717e0b51bdc4aa5eb1ac96d499867ac3bac5686354d8b072","observation_id":"3d249e67-19c9-4fb1-ae15-e96fdefe8885","resolution":{"observed_at":"2026-08-06T12:32:13.469763Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.443418Z","title":"Improving convergence in smoothed particle hydrodynamics simula- tions without pairing instability","venue":null,"work_id":"77045565-7a72-4493-baa4-92e9e181ca9b","year":2012},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.881629Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:ac1797c0a85ace0064b8b1c63b566875652cbc35d5b15f870d31bc3941e1c335","observation_id":"1f7ae4dd-6335-4513-bb62-f597b38a46f7","resolution":{"observed_at":"2026-08-06T12:32:13.448950Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.425869Z","title":"Implicit incompressible sph","venue":null,"work_id":"12bf0e32-4d1f-461f-8cb9-0378b2a555a3","year":2013},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.888214Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:5fcfdefa02776a07173a7d0da8675b32076e94a1f2068414a1f0bf83a00f1fea","observation_id":"ed1dbcc2-48d5-4526-b5f9-4521bdcb4574","resolution":{"observed_at":"2026-08-06T12:32:13.432243Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.405897Z","title":"Multi-level memory structures for simulating and rendering smoothed particle hydrodynamics","venue":null,"work_id":"aa5531fb-8939-4659-add4-4a2fb90474a9","year":2020},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.893955Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:61bee73935ec23bfe0d60a78021f74e1684d66fe2f895004fe6492d61c750899","observation_id":"2bb6c878-a7ab-40ab-bde9-9386c9efd2d0","resolution":{"observed_at":"2026-08-06T12:32:13.411689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.389147Z","title":"Asph modeling of material damage and failure","venue":null,"work_id":"381b9396-6b6f-4ea8-bba0-eaff0b684635","year":2010},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.904290Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:f8b53c0a1f7dfc9c99e57d034815517863cbc8d5d20ba8b8fd37c7ed9457aeb2","observation_id":"d52c3d7e-e5ef-4a94-a8b8-a98b8f8c74a6","resolution":{"observed_at":"2026-08-06T12:32:13.394080Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.372449Z","title":"A method of calculating radiative heat diffusion in particle simulations","venue":null,"work_id":"01443d54-2f54-4486-9865-7dce3f5175fd","year":1985},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.914964Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:d3d4f19a96b544aa9a39deaddf0ab92a84ec3593bf9ae6591eb2d91be9e233c0","observation_id":"163af9f1-5106-40bc-8e6b-73e9da173635","resolution":{"observed_at":"2026-08-06T12:32:13.377007Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.355003Z","title":"A consistent approach to particle shifting in the δ-plus-sph model","venue":null,"work_id":"4cd1e4bb-1753-4d94-bfc7-0976b4e6412e","year":2019},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.925585Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:fb65b0e6c58bc1be1f8a4f9164f227b5a045efeef5ae468a0e00c0fa5dbd8eee","observation_id":"6acfa066-6c8b-44a4-9179-04545d439956","resolution":{"observed_at":"2026-08-06T12:32:13.360494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.336628Z","title":"Implicit iterative particle shifting for meshless numerical schemes using kernel basis functions","venue":null,"work_id":"81d62e24-7e51-4c6b-ae3d-4329cdeb75c7","year":2022},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.934662Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:2233dde65a2d8a8af0ea170123f8dd069d3327743e68b41eda2176447e158359","observation_id":"80b87a4d-a54f-45fc-9d5f-38eb9b3bc337","resolution":{"observed_at":"2026-08-06T12:32:13.342625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.318356Z","title":"δ-sph model for simulating violent impact flows","venue":null,"work_id":"b47fae93-62e1-49d3-9c52-fdad5e237de8","year":2011},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.942102Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:803cc98c8feeb5384ef683bffbd8b4ccccca70364da2b9960fef5144bfcdd9cf","observation_id":"5aeea66f-4231-4a36-83a4-a135bb4f5c06","resolution":{"observed_at":"2026-08-06T12:32:13.324143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.301260Z","title":"Crksph–a conservative reproducing kernel smoothed particle hydrodynamics scheme","venue":null,"work_id":"5249c03a-6e71-4d91-8f6f-8affa6d3848c","year":2017},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.948931Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:44b2bbaa98faf7297e59ee3f80eaf68f24252cef3324c05ce4a3a20b9358ff51","observation_id":"a4276253-3c28-416a-90e8-0ed1fa669ef8","resolution":{"observed_at":"2026-08-06T12:32:13.307031Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.284331Z","title":"Learning to control pdes with differentiable physics","venue":null,"work_id":"fd9ab88b-c318-47fe-98cc-247c522e7771","year":2020},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.954743Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:4999b67bafabd0dbb604210b64f1246d7abfa15dbee32d82371f1cd3be14cfb0","observation_id":"03ae84bd-4d6c-41a7-a28f-508319524d66","resolution":{"observed_at":"2026-08-06T12:32:13.289108Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.266901Z","title":"Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates","venue":null,"work_id":"54ba5193-ba0f-4640-a707-ef618d7ffac0","year":2021},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.961408Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:aa3c710b983d5f8dbf24c4e2a8308e04cbc671bebfac92c44d40df839b5fc21b","observation_id":"01cf06f2-5dea-4ee5-97a8-6df62fe4fdec","resolution":{"observed_at":"2026-08-06T12:32:13.271998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.248836Z","title":"Adjoint sensitivity analysis for differential- algebraic equations: The adjoint dae system and its numerical solution","venue":null,"work_id":"cd828804-e812-40df-b415-5617c520263c","year":2003},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.967465Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:1e2828cbe54cc9aa888192d0365df892d8537c4733b683e67046abb30a134f97","observation_id":"88efe08d-c38c-4c78-a534-d7051f2c8373","resolution":{"observed_at":"2026-08-06T12:32:13.253722Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.231655Z","title":"A unifying mathematical definition of particle methods","venue":null,"work_id":"67865eee-561e-49bc-b3c6-d8396a7e9ec6","year":2023},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.975052Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:019312a4caa17b2109e40ada02c9d6d80acdfb08c7f8214349135952ccf27e3e","observation_id":"b235d35f-e779-45d2-bea6-2b41ef9e3889","resolution":{"observed_at":"2026-08-06T12:32:13.237670Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.213058Z","title":"Thuerey, B","venue":null,"work_id":"0c386ba8-e159-4b2f-8130-48a581588f52","year":2021},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.983452Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:b9046a496a2a482ed83de83a44392160e3594adf3a0f5d57acfc1c0dfce43e10","observation_id":"2e4259ab-64f3-4686-880f-03658dc9567e","resolution":{"observed_at":"2026-08-06T12:32:13.219457Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.194142Z","title":"The δ-ale-sph model: An arbitrary lagrangian- eulerian framework for the δ-sph model with particle shifting technique","venue":null,"work_id":"62608134-54e2-4f4a-9d34-15a00768201e","year":2021},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.990477Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:9cfa65b4705ab9206905c21466a2834e8b099a389ce05c835de5bcf1bfd0120d","observation_id":"8490b0e1-be52-4d58-b323-71ac339338d5","resolution":{"observed_at":"2026-08-06T12:32:13.201617Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.175846Z","title":"Numerical diffusive terms in weakly- compressible sph schemes","venue":null,"work_id":"95fd1d43-1730-4156-8666-abbf3e56727f","year":2012},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:11.996943Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:2af9bf55381f0208d18051bfbdf5bcdbc74bdabec5170caad5f7025991195926","observation_id":"d1d6582e-5216-466d-a336-05aff278bba0","resolution":{"observed_at":"2026-08-06T12:32:13.182772Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.158528Z","title":"Divergence-free smoothed particle hydrodynamics","venue":null,"work_id":"c392b3d7-046f-4f54-bf07-082eba17c5aa","year":2015},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.003400Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:b3a3248c4bc7347757b0b85ac18fdf0d754e95b98c6adbfb97830dd86a686f05","observation_id":"6be19d48-a772-47c1-a4b5-9ea486953c32","resolution":{"observed_at":"2026-08-06T12:32:13.163870Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.139408Z","title":"Incompressible sph method for simulating newtonian and non- newtonian flows with a free surface","venue":null,"work_id":"cbac4941-795f-43da-b1fc-c591c0522e2e","year":2003},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.010929Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:185ed3ae57752eee8b88a1e86fe64e404dc645aa6699a674d9cce551ef5294f9","observation_id":"bb527571-73ca-4dfa-b829-a6369499eea1","resolution":{"observed_at":"2026-08-06T12:32:13.145967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.120429Z","title":"An optimized source term formulation for incompressible sph","venue":null,"work_id":"5b57e722-3320-4ebc-a3e6-61dd4d44c173","year":2019},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.018833Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:0a7411a801e60c643516fd5beb1e7c28d9da2925ad3910fe440dca6c4ec37876","observation_id":"a86a5caf-f9ef-4f07-912c-1d94b002c38c","resolution":{"observed_at":"2026-08-06T12:32:13.125921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.104277Z","title":"A compatibly differenced total energy conserving form of sph","venue":null,"work_id":"930f420e-aa80-4e30-8e08-8133268356dd","year":2014},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.026478Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:2e3ab63ccdb8f0bff64c8b287c366b71a68627a4dbc2690711147746f4e36d6a","observation_id":"8bc10af8-bd97-40cf-b614-029c20317ff3","resolution":{"observed_at":"2026-08-06T12:32:13.109613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.085956Z","title":"Cosmological smoothed particle hydrodynamics simulations: the entropy equation","venue":null,"work_id":"f7c7775f-3dc2-4fd9-8133-f95013ba9bec","year":2002},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.034667Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:2c8f04300ebafc6bed18c2f28e06d6840c5ef68f923959bae7f654d339050514","observation_id":"ba48175c-bf23-4a70-81b9-ef1be7fb9bb0","resolution":{"observed_at":"2026-08-06T12:32:13.091936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.068768Z","title":"Conduction modelling using smoothed particle hydrodynamics","venue":null,"work_id":"c48deb2b-8f7c-4aeb-94c3-db13084999a9","year":1999},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.043675Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:a5247056833683a6d06484718af6f7ffa504c523aac6b5a17337731672d81b0d","observation_id":"1b2167dd-fcea-4dda-b208-952580966882","resolution":{"observed_at":"2026-08-06T12:32:13.074529Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.052623Z","title":"Sph compressible turbulence","venue":null,"work_id":"451e4cc2-92aa-4e0e-bdb0-ca392dc821ed","year":2002},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.050860Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:4652ddfbd88d6e4ecbadcc8401cb3c6ae72f2cae1bc714522b5cfdc84a73fd7a","observation_id":"dc04e645-7b5a-4da1-a060-0072708153c4","resolution":{"observed_at":"2026-08-06T12:32:13.057771Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.035358Z","title":"Von neumann stability analysis of smoothed particle hydrodynamics—suggestions for optimal algorithms","venue":null,"work_id":"cc856717-e65e-4666-b9ef-07c85dac1adb","year":1995},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.057579Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:0eb66c7667e43f8552701a04c188cd7d5db19b734759139e0f6eecf819565e3d","observation_id":"1161ce79-c4ef-4b74-83e0-2c5eb17986c3","resolution":{"observed_at":"2026-08-06T12:32:13.041214Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:13.016921Z","title":"Inviscid smoothed particle hydrodynamics","venue":null,"work_id":"718a628b-7d5a-4376-9fb7-5a8015baa414","year":2010},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.065285Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:6da8b394f64472a9f6b5b90f303eb10f6a1e8d792b33ad2d4e326b27137e3ba6","observation_id":"a17fa34c-b581-4cda-a25d-4a6a190b493a","resolution":{"observed_at":"2026-08-06T12:32:13.022769Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.999797Z","title":"A general class of lagrangian smoothed particle hydrodynamics methods and implica- tions for fluid mixing problems","venue":null,"work_id":"a358359f-dcdd-4111-9e63-27c3dc162f63","year":2013},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.072420Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:25f2b5ab3f16b73109090d88e16bc92cee4e458575c66f86fb45203c0a781643","observation_id":"688f5720-d241-4998-bcf0-c04521a4c707","resolution":{"observed_at":"2026-08-06T12:32:13.006038Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.982926Z","title":"Semi-analytic boundary handling below particle resolution for smoothed particle hydrodynamics","venue":null,"work_id":"f3ca1005-8192-49cd-882e-f6c82a3b9391","year":2020},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.078752Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:d2095e83e2db80e57fdfa5f624fac9d5cf45a8ff16cb4f8e201af171ea4033a1","observation_id":"49dc4bfd-f9b8-4c1f-af3c-9f15c7aae61e","resolution":{"observed_at":"2026-08-06T12:32:12.989457Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.967198Z","title":"Modified dynamic boundary conditions (mdbc) for general-purpose smoothed particle hydrodynamics (sph): Application to tank sloshing, dam break and fish pass problems","venue":null,"work_id":"454c780e-dcab-493c-89f1-fd6c80703c06","year":2022},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.086494Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:c3c6b6492f57287a401891bd01300da248c06c9623362cb7152c3ad0f4a1c99b","observation_id":"f431e98f-6e59-4b2b-bc9b-28f22ecfb484","resolution":{"observed_at":"2026-08-06T12:32:12.972114Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.949725Z","title":"Particle-based fluid simulation for interactive applications","venue":null,"work_id":"b5af8eaf-6262-4f0c-87b4-014cacbf1395","year":2003},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.096882Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:c5ed0e177b88e291a0fd9a3837062c846de6efb4eeb6b188b968351d54c00975","observation_id":"a53dcc4e-2175-421a-afdc-90065dff6ce8","resolution":{"observed_at":"2026-08-06T12:32:12.955229Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.931051Z","title":"Eulerian incompressible smoothed particle hydrodynamics on multiple gpus.Computer Physics Communications, 273:108263, 2022","venue":null,"work_id":"9e861bd0-3112-4117-bd23-ef0aac368cdc","year":2022},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.106855Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:c739cfc50952b451a3cf00e8390f9e676231b43a02970e4ba581aaed3e49bb1d","observation_id":"f252f200-3c8c-4eaa-8704-66ecf691d394","resolution":{"observed_at":"2026-08-06T12:32:12.937830Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.915027Z","title":"Mls pressure boundaries for divergence-free and viscous sph fluids","venue":null,"work_id":"6e80b478-531a-4ff4-853b-edf02fd4f348","year":2018},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.116667Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:4abfa2131a7acc02bf4a82822579824ae0d8053913fb362a2aa76911c1e27d5d","observation_id":"491aef1b-3dd6-4c61-aff4-5185d8e7f226","resolution":{"observed_at":"2026-08-06T12:32:12.920052Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.896879Z","title":"An improved non-reflecting outlet boundary condition for weakly-compressible sph","venue":null,"work_id":"ee6b0078-4e21-463c-872d-ff66a177586a","year":2020},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.125995Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:21a809d62366ed38909b0a363c69edf7352106d6b6e3c55a4d956d607cf9f390","observation_id":"d8c615f6-9682-4229-a236-3aa9cc3ef196","resolution":{"observed_at":"2026-08-06T12:32:12.903419Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.878593Z","title":"Multi-level-memory structures for adaptive SPH simulations","venue":null,"work_id":"fd841878-4fd6-4559-9498-477af45fc77b","year":2019},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.134920Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:0fb224f52d2ee1c7cf058d12a0bc13fae37e3a2cdd8a66e60d9246139e3fc1e5","observation_id":"b89e9dd7-9b12-49f3-aad3-0e54b2d303b0","resolution":{"observed_at":"2026-08-06T12:32:12.884204Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.860954Z","title":"A hybrid framework for fluid flow simulations: Combining sph with machine learning","venue":null,"work_id":"f750b4b0-ae15-4916-be33-ab579b977238","year":2023},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.144012Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:b39c7b36eeee39b33c7e8f99e66cf868d4d2ed3c9490d127506a9703d76ffe1f","observation_id":"d4338dec-2cbf-438b-a7e7-fc4595a4d9dc","resolution":{"observed_at":"2026-08-06T12:32:12.866771Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.833223Z","title":"Splinecnn: Fast geometric deep learning with continuous b-spline kernels","venue":null,"work_id":"a477b7cd-ff94-4d3a-9b04-013ff0534100","year":2018},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.153965Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:ad5c88627ef28c07dcb13619127487e1648467ff2a94c7a19974e903f33bfc08","observation_id":"c432f9ae-841f-4cc5-a38e-9084198880c4","resolution":{"observed_at":"2026-08-06T12:32:12.839592Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.814676Z","title":"Efficient coding of the minimum image convention","venue":null,"work_id":"8e7b5bd8-2536-4f94-8242-6a517b473be1","year":2013},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.161954Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:3515b74258496bc90e7795b132463c8ada0cca1201485387b952679543bfefc2","observation_id":"992969da-a40a-46a4-bf45-156c30cebca6","resolution":{"observed_at":"2026-08-06T12:32:12.820405Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.796918Z","title":"Constrained neighbor lists for sph-based fluid simulations","venue":null,"work_id":"1574a7b5-6299-4e14-94f7-12e57adc50f6","year":2016},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.168869Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:d91661ec7bb29aa34a03fcc0442e247e083da911676b9a0d5aa84ef7ff082909","observation_id":"ac32a648-d5e7-4599-9dcc-ee6aba63e87f","resolution":{"observed_at":"2026-08-06T12:32:12.803121Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.778803Z","title":null,"venue":null,"work_id":"05998808-1249-46be-968e-817f57f74122","year":2024},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.176229Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:fe818c771ae262219672a9f049adf57fd76f19cb47a9f1dea8d08c51c69388f7","observation_id":"4e2ab71b-f1f4-496b-8291-6d4e3d1c75a7","resolution":{"observed_at":"2026-08-06T12:32:12.784005Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.758459Z","title":"The complexity of partial derivatives","venue":null,"work_id":"b946a9f7-cb0c-41d5-b6f6-5458e88d3f9c","year":1983},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.182602Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:30bdbbe4227d62b7f765bf66707ca2d597004463c281fd48033e4ac3a8db7b06","observation_id":"ce27f395-a97b-4f61-9edd-112af624fca7","resolution":{"observed_at":"2026-08-06T12:32:12.765833Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.733607Z","title":"Kingma and Jimmy Ba","venue":null,"work_id":"c87f4648-3469-43c6-947a-59e0624dc1dd","year":2015},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.188632Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:623a20b9cbb6782e29c5542b3a21a1c1eec0c443526b85ba7305bcb09b348f37","observation_id":"6fc1be48-045b-4311-b5cf-f66edbba4b3b","resolution":{"observed_at":"2026-08-06T12:32:12.740756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.710818Z","title":"Learnable fourier features for multi- dimensional spatial positional encoding","venue":null,"work_id":"b31c08ec-f777-40ac-a177-b2d9616bb370","year":2021},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.196084Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:270501c1bee8c0ff73d351c897e3aeb126c51167cfa61989e49fbcf28a6b6d66","observation_id":"3e385926-717b-4e28-a3b0-71cd39b0cf51","resolution":{"observed_at":"2026-08-06T12:32:12.717180Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.687692Z","title":"Smith, Ayya Alieva, Qing Wang, Michael P","venue":null,"work_id":"c64ac68b-b2d4-41b7-a803-ee1ef7a8f806","year":2021},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.206797Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:a95dd8e00b7b0c2fd4f0ff8dfc4bbb27901385cacc7c530690677b21388ec65d","observation_id":"00ab3ad5-13b9-45e2-97c8-fde28406d8d0","resolution":{"observed_at":"2026-08-06T12:32:12.696017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.654565Z","title":"Worrall, and Max Welling","venue":null,"work_id":"9bff64b5-f333-40e9-9e6c-f68f108c216f","year":2022},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.214379Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:c424e9d0a2155d7bf201a8b0225a58fe5e174b1a8cb80b011b110b59c32d5b1e","observation_id":"066baef7-c227-459f-bc9b-e0e06fcf9a64","resolution":{"observed_at":"2026-08-06T12:32:12.674307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.630633Z","title":"Fourier features let networks learn high frequency functions in low dimensional domains","venue":null,"work_id":"2ad486a2-b535-4993-a338-829d8b16efee","year":2020},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.229385Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:2c83dd22d66190a14e4ab9e68928753494720a68ec4680b0bcdf9e72fc88cc85","observation_id":"c5478e65-fe64-4878-b2df-fc1ee0ac4dc3","resolution":{"observed_at":"2026-08-06T12:32:12.639240Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.605643Z","title":"Differentiability in unrolled training of neural physics simulators on transient dynamics","venue":null,"work_id":"52166e49-2bb2-4877-ae86-c6d78775983b","year":2025},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.237466Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:d20b5f2667b79165ede54d99618ced86b4db9e906b4782c1e20a41f2e496ea93","observation_id":"a1435b48-b611-4a9d-ae53-bb29013d80f6","resolution":{"observed_at":"2026-08-06T12:32:12.614810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.571800Z","title":"Diehl, G","venue":null,"work_id":"2990cb99-235a-4ef7-adb8-87782f0d1623","year":2015},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.247014Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:13df03d2ba1a06d8329c9abd840fbd037cc2dfe4b6b3c38cc201dee82981976b","observation_id":"4dea4732-2ecd-4479-a8e7-3c921ac15903","resolution":{"observed_at":"2026-08-06T12:32:12.582604Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.550152Z","title":"Infinite continuous adaptivity for incom- pressible SPH","venue":null,"work_id":"522717cb-20df-4c3e-ad60-a9f30c22ada0","year":2017},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.255416Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:0099890d2df9035cf03a405946beb8b50324b9185688c7ce7d896191b71e5654","observation_id":"a01ecc38-da05-4ca8-839f-afedb6c12845","resolution":{"observed_at":"2026-08-06T12:32:12.557792Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.520079Z","title":"Fast and accurate sph modelling of 3d complex wall boundaries in viscous and non viscous flows","venue":null,"work_id":"5509ef17-726b-41f4-bfcb-9f7ef5e9c020","year":2019},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.267311Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:1e6498738ba648b9c77095c3c80f36933bf2816baec5bc93460842f6e0ab23c6","observation_id":"60ef4931-a5a9-4ba9-8e0e-f827dbc8d534","resolution":{"observed_at":"2026-08-06T12:32:12.529635Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.495510Z","title":"Versatile rigid-fluid coupling for incompressible sph","venue":null,"work_id":"cb049782-d2c2-4f1a-9974-70ea8c4987f5","year":2012},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.275803Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:32a28420c4dc704b19c08a4b41e9f6bd8476ca399a4c3da96fbebab0ede0df24","observation_id":"1af80a10-f5b1-4c28-bc37-af906e2c6c13","resolution":{"observed_at":"2026-08-06T12:32:12.502907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.474977Z","title":"Unified semi-analytical wall boundary conditions applied to 2-d incompressible sph","venue":null,"work_id":"60db0ba5-07ca-4592-a6df-c09d2cfd5a48","year":2014},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.285016Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:c505c077a9a825f76514020c468f999cf778b8ba49734586c3ee1a506dc0f5d4","observation_id":"4f09db75-0bdd-4343-8694-8f9411f02d2b","resolution":{"observed_at":"2026-08-06T12:32:12.482253Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.453434Z","title":null,"venue":null,"work_id":"3e0b513e-7fd8-45c8-8b50-9c282935e819","year":2010},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.291605Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:d235b163dced7ce78a19e84d1827bc40d68ad9c65de56291fa76c2a6515aa455","observation_id":"9f0281ef-7d56-4af1-8d32-d20cdfb1eb6a","resolution":{"observed_at":"2026-08-06T12:32:12.460045Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.425345Z","title":"Simulating free surface flows with sph","venue":null,"work_id":"4c286e61-193e-4a52-896b-8affe32cdee4","year":1994},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.298908Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:204ac6b18c728a3f7e55e91100d2a11c687a3c5b3c5da757ac1837bf91dd0556","observation_id":"2c4b5e85-16fd-4e39-a091-8396cb1b825c","resolution":{"observed_at":"2026-08-06T12:32:12.434398Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:32:12.393967Z","title":null,"venue":null,"work_id":"4d340a3b-451f-426e-bcf5-d8dcb8c8aa6d","year":2017},"citing_paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:12.306068Z"},"links":{"citing_paper":"/paper/2507.21684"},"observation_digest":"sha256:918ac5d70effbb229ac2338060a57cd95c4095ab215f451624152f26c2eff6cc","observation_id":"0dde0bef-791d-4fad-b0ce-06a4298e36fe","resolution":{"observed_at":"2026-08-06T12:32:12.402882Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.21684","last_updated":"2025-07-29T10:54:27Z","latest_version":1,"primary_category":"physics.flu-dyn","snapshot_observed_at":"2026-08-06T12:32:04.765612Z","submitted_at":"2025-07-29T10:54:27Z","title":"diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning"},"reference_resolution":{"displayed":88,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":0,"verified_fuzzy":73},"total_outbound_references":88},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 88 of 88 outbound references and 1 inbound Pith citation observation for arXiv:2507.21684."}