{"as_of":"2026-08-16T21:46:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:77280f854648807ec6610f5f9c85dc8c424fd96483f5828eb93a8737cbf94510","coverage":[{"denominator":24,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":24,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T05:42:34.253155Z","state":"measured"},{"denominator":25,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":25,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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":"2026-06-30T21:02:11.486234Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-06-30T21:05:04.043340Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"cited_work":{"arxiv_id":"2504.20019","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.20019","snapshot_observed_at":"2026-06-30T21:05:04.043340Z","title":"Modelling of underwater vehicles using physics-informed neural networks with control,","venue":null,"work_id":"753d100d-8597-4981-93f3-cc5958480844","year":2025},"citing_paper":{"arxiv_id":"2605.14683","last_updated":"2026-05-14T10:57:38Z","snapshot_observed_at":"2026-07-06T23:26:04.286377Z","submitted_at":"2026-05-14T10:57:38Z","title":"SeaVis: Modeling and Control of a Remotely Operated Towed Vehicle for Seabed Visualization and Mapping","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-30T21:02:11.486234Z"},"links":{"cited_paper":"/paper/2504.20019","citing_paper":"/paper/2605.14683"},"observation_digest":"sha256:5304391c0e0245b0633fc516670bea807fe24d21cd7f8031b412b1e922ea41ce","observation_id":"47d706f8-9de7-4b21-ab23-c90e07e5d8e1","resolution":{"observed_at":"2026-06-30T21:05:04.044895Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2504.20019/citation-record","integrity":"/paper/2504.20019/integrity","json":"/paper/2504.20019/citation-record.json","paper":"/paper/2504.20019"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T05:42:34.541017Z","title":"Unav-sim: A visually realistic underwater robotics simulator and synthetic data-generation framework,","venue":null,"work_id":"0e6f5e7b-138d-4e12-b4ff-5c817576cd59","year":2023},"citing_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T05:42:34.173136Z"},"links":{"citing_paper":"/paper/2504.20019"},"observation_digest":"sha256:51b3c335f8182857b81f62bd0f4bd87af06cc2a8f4b50c2c1d1a55c54e532947","observation_id":"046c2682-c78a-4761-8ad1-d75ba7311666","resolution":{"observed_at":"2026-08-16T05:42:34.544992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T05:42:34.529593Z","title":"Deep sea underwater robotic exploration in the ice-covered arctic ocean with auvs,","venue":null,"work_id":"66ba6190-651d-4d0b-91f0-bc3b31593b24","year":2008},"citing_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T05:42:34.178110Z"},"links":{"citing_paper":"/paper/2504.20019"},"observation_digest":"sha256:bd2fc4576520c8a501316520e03973fa6cc334ddb5d5cb312cc4ada9419daf22","observation_id":"e2efb473-dc6e-42fc-b28a-a9c3dfab5a57","resolution":{"observed_at":"2026-08-16T05:42:34.533556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T05:42:34.518654Z","title":"Visual tracking nonlinear model predictive control method for autonomous wind turbine inspection,","venue":null,"work_id":"993b7557-fe83-4647-9066-f05a61f42bed","year":2023},"citing_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T05:42:34.182276Z"},"links":{"citing_paper":"/paper/2504.20019"},"observation_digest":"sha256:cc2d113e4cf3194bd397d18cd6e4515838dd25002b5b5563ee75d9654a15913f","observation_id":"d40bd01e-611d-40cf-bca2-915c4822fe73","resolution":{"observed_at":"2026-08-16T05:42:34.522534Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T05:42:34.505983Z","title":"Estimation of External Force Acting on Underwater Robots,","venue":null,"work_id":"979e2cfe-f686-4de2-b4c8-5232651252c0","year":2024},"citing_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T05:42:34.186266Z"},"links":{"citing_paper":"/paper/2504.20019"},"observation_digest":"sha256:67d36fbd9c4fb509e57da716a60f553eeaba908ac812ee8e197a0c842d209f91","observation_id":"2c5acc9c-e373-482f-9bcd-28badafc14e3","resolution":{"observed_at":"2026-08-16T05:42:34.509854Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T05:42:34.190473Z","title":"Physics-informed machine learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T05:42:34.190473Z"},"links":{"citing_paper":"/paper/2504.20019"},"observation_digest":"sha256:1a95cd75afb88e60b50afe17349d74154d9599b3eabb60042edbb5d0b394ca89","observation_id":"124ca721-98af-403d-86e2-4d13ff3f846d","resolution":{"observed_at":"2026-08-16T05:42:34.190473Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.12952","last_updated":"2025-06-12T22:24:25Z","snapshot_observed_at":"2026-08-16T12:16:19.258381Z","submitted_at":"2025-04-17T13:52:55Z","title":"Safe Physics-Informed Machine Learning for Dynamics and Control","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.12952","snapshot_observed_at":"2026-08-16T05:42:34.194243Z","title":"Safe physics- informed machine learning for dynamics and control,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T05:42:34.194243Z"},"links":{"cited_paper":"/paper/2504.12952","citing_paper":"/paper/2504.20019"},"observation_digest":"sha256:12b1439e21eb0d0012d480696400eb5700469b3e1312cc79e4168c0f0cc3185b","observation_id":"4b8bb0fb-db48-42cf-96ef-abe3e72ed9aa","resolution":{"observed_at":"2026-08-16T05:42:34.194243Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.02556","last_updated":"2022-05-31T19:13:41Z","snapshot_observed_at":"2026-08-16T18:33:33.169455Z","submitted_at":"2021-04-06T14:55:23Z","title":"Physics-Informed Neural Nets for Control of Dynamical Systems","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.02556","snapshot_observed_at":"2026-08-16T05:42:34.198513Z","title":"Physics-Informed Neural Nets for Control of Dynamical Systems,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T05:42:34.198513Z"},"links":{"cited_paper":"/paper/2104.02556","citing_paper":"/paper/2504.20019"},"observation_digest":"sha256:7f983d0b72111cd6c3e313e1c8c0c798ccff4664bb74a1ab053a5df431f42e1a","observation_id":"891e46b1-ab26-4f99-aca1-55647ae08dcf","resolution":{"observed_at":"2026-08-16T05:42:34.198513Z","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":"2026-08-16T05:42:34.487635Z","title":null,"venue":null,"work_id":"6abb0ceb-bdc5-4b67-9bde-b3deb83a7406","year":2021},"citing_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T05:42:34.202397Z"},"links":{"citing_paper":"/paper/2504.20019"},"observation_digest":"sha256:879960acb057d68fa0e83b47bb3d6f7bdc976e3a763aed97d904451da154d7d3","observation_id":"41adbe99-8cef-4998-ba2f-74e286ed58b0","resolution":{"observed_at":"2026-08-16T05:42:34.491656Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T05:42:34.474820Z","title":"Empowering autonomous underwater vehicles using learning-based model predictive control with dynamic forgetting gaussian processes,","venue":null,"work_id":"43123467-a416-4064-8e7e-89a91c3dec8f","year":2025},"citing_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T05:42:34.206029Z"},"links":{"citing_paper":"/paper/2504.20019"},"observation_digest":"sha256:60dd786a9658e7efbb0e9af0146984335ce9ca8afffea07be1e4860d230c4a8f","observation_id":"1c111d92-9caf-40e5-9390-0d81e9a67631","resolution":{"observed_at":"2026-08-16T05:42:34.479455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T05:42:34.461646Z","title":"Adaptive robust control integrated with gaussian processes for quadrotors: Enhanced accuracy, fault tolerance and anti-disturbance,","venue":null,"work_id":"fbb13c5f-173d-413f-9816-cc55948d18e8","year":2025},"citing_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T05:42:34.209608Z"},"links":{"citing_paper":"/paper/2504.20019"},"observation_digest":"sha256:3f4375f4125cbce49e608199b80efdac7b99b2eecf55a980c1592be1208ef401","observation_id":"3ad47911-157b-4298-9850-a9fa2cba3bee","resolution":{"observed_at":"2026-08-16T05:42:34.466348Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T05:42:34.449928Z","title":"A data-driven tracking control framework using physics-informed neural networks and deep reinforcement learning for dynamical systems,","venue":null,"work_id":"51794da8-1fbc-4c24-ac0e-10dbfca95ed8","year":2024},"citing_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T05:42:34.213080Z"},"links":{"citing_paper":"/paper/2504.20019"},"observation_digest":"sha256:bae78dac6d78379f16f995938ddb256c9da03cd449a877868a07452953e7c195","observation_id":"c44ee34b-7108-44a4-973a-18a6c88e08fb","resolution":{"observed_at":"2026-08-16T05:42:34.454145Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T05:42:34.438181Z","title":"Ramp-net: A robust adaptive mpc for quadrotors via physics-informed neural network,","venue":null,"work_id":"3ec6c623-7bdb-4f16-9a90-03c650185408","year":2023},"citing_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T05:42:34.216534Z"},"links":{"citing_paper":"/paper/2504.20019"},"observation_digest":"sha256:6556a8dc8597398cfd79d0f55096ee327aa9e0316e5028e85a163c4114561bd0","observation_id":"2e7f1c85-5785-4c2d-be66-97dd885006ee","resolution":{"observed_at":"2026-08-16T05:42:34.441791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T05:42:34.426344Z","title":"Combining physics and deep learning to learn continuous-time dynamics models,","venue":null,"work_id":"030b6039-b90c-4512-b753-2cb5f281966b","year":2023},"citing_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T05:42:34.219747Z"},"links":{"citing_paper":"/paper/2504.20019"},"observation_digest":"sha256:6e54490e098872eaadbbccafc74ce8252c7c02ff11de16d0f49bd74f4c3b804a","observation_id":"1bfb4d32-f83d-4010-b4e2-a0543b096241","resolution":{"observed_at":"2026-08-16T05:42:34.430437Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T05:42:34.414744Z","title":"Development and Implementation of Physics-Informed Neural ODE for Dynamics Model- ing of a Fixed-Wing Aircraft Under Icing/Fault,","venue":null,"work_id":"0c510c04-915a-486c-bcc5-90b34b81334f","year":2024},"citing_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T05:42:34.222456Z"},"links":{"citing_paper":"/paper/2504.20019"},"observation_digest":"sha256:74b8804763b0013f92597ce1c8ab28634267f329be42a3bb6705b23adee61855","observation_id":"78876220-66a1-4425-ae4c-c766b12dfff7","resolution":{"observed_at":"2026-08-16T05:42:34.418337Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T05:42:34.225325Z","title":"Neural ordinary differential equations,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T05:42:34.225325Z"},"links":{"citing_paper":"/paper/2504.20019"},"observation_digest":"sha256:4521fe6343041a35cefd9ae977d764f924eb4026d72fefafed93c6d8d3254b26","observation_id":"5ee864ac-d537-423b-a34c-3fba37aa5936","resolution":{"observed_at":"2026-08-16T05:42:34.225325Z","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":"2026-08-16T05:42:34.396610Z","title":"Research on Modeling Method of Autonomous Underwater Vehicle Based on a Physics- Informed Neural Network,","venue":null,"work_id":"ce82a627-c30f-4b18-ace8-c046522e46f8","year":2024},"citing_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T05:42:34.228177Z"},"links":{"citing_paper":"/paper/2504.20019"},"observation_digest":"sha256:81fd3d0e87b3ebd8499f828bc49538f51dee4bcac682229689204e8c225121a9","observation_id":"8ab41418-67d8-40f4-b3f8-69a55430d1a1","resolution":{"observed_at":"2026-08-16T05:42:34.400625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T05:42:34.385884Z","title":"Sim-to- Real of Soft Robots with Learned Residual Physics,","venue":null,"work_id":"b6e9d81e-c563-4ece-99f8-e764bb1c72e6","year":2024},"citing_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T05:42:34.231113Z"},"links":{"citing_paper":"/paper/2504.20019"},"observation_digest":"sha256:466b0cea20ac161887de7e1a792c051c387eeba74b524638ceb5eeda720b484a","observation_id":"0daa9087-5d4b-4885-a40c-9a0620085d75","resolution":{"observed_at":"2026-08-16T05:42:34.389662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T05:42:34.375192Z","title":"Domain-decoupled Physics-informed Neural Networks with Closed- form Gradients for Fast Model Learning of Dynamical Systems,","venue":null,"work_id":"f7518982-6f38-4480-a521-a08dbe391313","year":2024},"citing_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T05:42:34.234012Z"},"links":{"citing_paper":"/paper/2504.20019"},"observation_digest":"sha256:896d3262d6bcef3992fa2c96d431d65c41fee078dacce542824104c3467d18f2","observation_id":"5c4c2725-d22c-43e6-91c8-d171af9df11a","resolution":{"observed_at":"2026-08-16T05:42:34.378938Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T05:42:34.362387Z","title":"Physics-Informed Neural Networks with Skip Connections for Model- ing and Control of Gas-Lifted Oil Wells,","venue":null,"work_id":"2eec4c95-c175-43d6-9831-d9cacf7668c6","year":2024},"citing_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T05:42:34.237152Z"},"links":{"citing_paper":"/paper/2504.20019"},"observation_digest":"sha256:09da9d70fc721999e05e15e5e9f23bded8991c0a2cd2788ea8cf059ca4000623","observation_id":"d1eed7a4-3675-45cf-8b1f-36a66677757c","resolution":{"observed_at":"2026-08-16T05:42:34.366890Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T05:42:34.349732Z","title":"ConFIG: Towards Conflict-free Training of Physics Informed Neural Networks,","venue":null,"work_id":"dfb68011-bfc1-480a-bed4-ef36b123c843","year":2024},"citing_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T05:42:34.239934Z"},"links":{"citing_paper":"/paper/2504.20019"},"observation_digest":"sha256:03c0f99b0c8ab22b12d1ba89833df40c9fac7136641039dfc237724638c057bc","observation_id":"22bfcc22-4ae1-42de-8ab2-c433b6741ad4","resolution":{"observed_at":"2026-08-16T05:42:34.353815Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T05:42:34.337872Z","title":"Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulators,","venue":null,"work_id":"f79e7529-9395-44bf-92de-b45a0b61115f","year":2022},"citing_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T05:42:34.242749Z"},"links":{"citing_paper":"/paper/2504.20019"},"observation_digest":"sha256:65b4ee0f809933cdcad78a8f5c07c9b1825e6e0c0e3c00cfd6f721d2d279e6e9","observation_id":"e233c72b-42fa-4880-9f7e-08e5cee561f3","resolution":{"observed_at":"2026-08-16T05:42:34.341800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T05:42:34.325772Z","title":"Handbook of marine craft hydrodynamics and motion control,","venue":null,"work_id":"c6af90a0-0c21-4592-98df-82045cfad257","year":2021},"citing_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T05:42:34.245585Z"},"links":{"citing_paper":"/paper/2504.20019"},"observation_digest":"sha256:d5895d94b3052858c0a0a842e1d7ed85613ba62a0377b37d58edf5d695e55b1b","observation_id":"09f98d3c-dc5e-43d2-a937-6dbab081a448","resolution":{"observed_at":"2026-08-16T05:42:34.329721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T05:42:34.312601Z","title":"Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,","venue":null,"work_id":"cbe8bbd0-bbf0-4bb1-b36e-fdc13fbc5cd0","year":2019},"citing_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T05:42:34.248940Z"},"links":{"citing_paper":"/paper/2504.20019"},"observation_digest":"sha256:3fb8019ea0fac91ea6a854d73e4f151db7a99f600270c2bfad2cc2eb5ec773d0","observation_id":"23b3f691-1db7-4210-b55f-a4e7af07ea48","resolution":{"observed_at":"2026-08-16T05:42:34.317700Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T05:42:34.299217Z","title":"Layer Normalization,","venue":null,"work_id":"5d4cce2c-b8d4-4c9d-86a2-cbf2dd9f566e","year":2016},"citing_paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T05:42:34.253155Z"},"links":{"citing_paper":"/paper/2504.20019"},"observation_digest":"sha256:7d67fb153446323d1d63b47ea83fc9551c982921ca2a9016ca4b73130876df30","observation_id":"f35a46e1-cdb4-47bd-9385-c1c737464c8f","resolution":{"observed_at":"2026-08-16T05:42:34.304452Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2504.20019","last_updated":"2025-04-28T17:38:57Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T05:34:58.375103Z","submitted_at":"2025-04-28T17:38:57Z","title":"Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control"},"reference_resolution":{"displayed":24,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":0,"verified_fuzzy":19},"total_outbound_references":24},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 1 inbound Pith citation observation for arXiv:2504.20019."}