{"as_of":"2026-08-07T19:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1ba21aba4b7ad09aea66cfd8d4ea9a7e32687ca3b2ab0377688b437da0517bf9","coverage":[{"denominator":73,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":73,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T17:58:21.109252Z","state":"measured"},{"denominator":75,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":75,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T05:22:12.534960Z","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":"2026-07-02T03:46:32.360685Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.10363","snapshot_observed_at":"2026-08-03T05:22:12.534960Z","title":"D., Kinch, B., Klobusicky, J., Hsieh, M","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.02788","last_updated":"2026-06-08T20:08:07Z","snapshot_observed_at":"2026-08-03T05:22:09.531006Z","submitted_at":"2026-02-02T20:45:07Z","title":"Structure-Preserving Learning Improves Geometry Generalization in Neural PDEs","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-03T05:22:12.534960Z"},"links":{"cited_paper":"/paper/2509.10363","citing_paper":"/paper/2602.02788"},"observation_digest":"sha256:ce1a75db0ffe8df2b1055da1af9df2107c5c6eeb07648596711dfb51d3624bce","observation_id":"41071298-b392-449a-bf63-47be56aa180c","resolution":{"observed_at":"2026-08-03T05:22:12.534960Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"cited_work":{"arxiv_id":"2509.10363","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2509.10363","snapshot_observed_at":"2026-07-02T03:46:32.360685Z","title":null,"venue":null,"work_id":"f734cb3f-f518-4c1c-9b65-9fd2bcc698e2","year":2025},"citing_paper":{"arxiv_id":"2606.03756","last_updated":"2026-06-02T15:06:25Z","snapshot_observed_at":"2026-08-06T21:48:58.774893Z","submitted_at":"2026-06-02T15:06:25Z","title":"Neural Navigation Functions for Zero-Shot Generalizable Motion Planning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-06-28T09:44:54.897829Z"},"links":{"cited_paper":"/paper/2509.10363","citing_paper":"/paper/2606.03756"},"observation_digest":"sha256:955e962f0bd1fd0392a2963254c2ed6ad0399eb4e8a04b8d6b981da070e40d05","observation_id":"6956b51e-a6f3-48da-94d9-724a4f114256","resolution":{"observed_at":"2026-07-02T03:46:32.362594Z","resolver_source":"arxiv_id","status":"verified_exact"},"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"}}],"links":{"evidence":"/evidence","html":"/paper/2509.10363/citation-record","integrity":"/paper/2509.10363/integrity","json":"/paper/2509.10363/citation-record.json","paper":"/paper/2509.10363"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T17:58:14.270486Z","title":"Data-driven whitney forms for structure-preserving control volume analysis","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:14.270486Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:87f19b218762b9ae0ae5c6ac94b038ba1e8dcc111ce1988185b4649d7e0ea338","observation_id":"54394fcf-759d-4c50-8501-8f5377748209","resolution":{"observed_at":"2026-08-04T17:58:14.270486Z","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":"2026-08-04T17:58:14.307656Z","title":"Riemannian lp center of mass: existence, uniqueness, and convexity","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:14.307656Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:c68ef5f38cf119ce32e5e78784aa4bc5e384475f1b4963ae118421b35cab8899","observation_id":"89c65a93-5f4e-4a95-b588-56f01eba2ad7","resolution":{"observed_at":"2026-08-04T17:58:14.307656Z","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":"2026-08-04T17:58:14.350960Z","title":"Model-based solution techniques for the source localization problem","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:14.350960Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:9ab9cd233a733283dd0fb3c049b63eaa2e30046e79a5c89a986f64ffecddccc3","observation_id":"9d92e71d-0f95-4965-82c1-09d5b4847361","resolution":{"observed_at":"2026-08-04T17:58:14.350960Z","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":"2026-08-04T17:58:14.433626Z","title":"Finite element exterior calculus","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:14.433626Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:34ac425de98e78f9d8945fa5d6c85ab3a8d5fbefd9db906babe061af16920cbf","observation_id":"218b94c7-19d3-48bb-9068-f020fee25a92","resolution":{"observed_at":"2026-08-04T17:58:14.433626Z","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":"2026-08-04T17:58:14.493267Z","title":"Finite element exterior calculus, 25 Fig","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:14.493267Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:8a342efd5a4fe542c78c4c8550ca7986f7b5ed49e312d280d142998134bc9e25","observation_id":"29b218b3-540b-4a65-a90b-be1afef75031","resolution":{"observed_at":"2026-08-04T17:58:14.493267Z","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":"2026-08-04T17:58:14.545139Z","title":"Solving inverse problems using data-driven models","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:14.545139Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:a3dc44746724c0dc18d526f76904c191d469f706dce6dc7c21a13d6202a9249a","observation_id":"dc77511e-576b-45eb-bb14-e8267e487a34","resolution":{"observed_at":"2026-08-04T17:58:14.545139Z","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":"2026-08-04T17:58:14.653901Z","title":"Iterative methods for approximate solution of inverse problems , volume 577","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:14.653901Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:59ad9e70963e361dfef7e636a2425579bcd9fe20f85709f2a1b306766e173b9b","observation_id":"e5a6b347-c688-4317-9f75-43189f97b4c4","resolution":{"observed_at":"2026-08-04T17:58:14.653901Z","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":"2026-08-04T17:58:14.742493Z","title":"Inverse source problems in transport equations","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:14.742493Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:fd3033d33817a8044a53da52bcc5bb97fad0e583515ebac066a9db3fe68f2b0b","observation_id":"a416feb1-8cfa-4098-9c93-0def291491cf","resolution":{"observed_at":"2026-08-04T17:58:14.742493Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.04934","last_updated":"2023-03-02T23:50:34Z","snapshot_observed_at":"2026-08-04T09:39:04.626946Z","submitted_at":"2022-09-08T17:35:30Z","title":"Clifford Neural Layers for PDE Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.04934","snapshot_observed_at":"2026-08-04T17:58:14.825172Z","title":"Clifford neural layers for pde modeling","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:14.825172Z"},"links":{"cited_paper":"/paper/2209.04934","citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:9a2988efce070cd224977b9e5f5ed752fe7fc427e1c34ebce96fe0309ffa08d5","observation_id":"5580ac2d-7b95-4b23-b21e-70f271f5c025","resolution":{"observed_at":"2026-08-04T17:58:14.825172Z","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":"2026-08-04T17:58:14.910102Z","title":"Physics-informed neural networks (pinns) for fluid mechanics: A review","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:14.910102Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:84f0fb1088866fe420072459754dc74391899393000e46316ea612ba461f4b8f","observation_id":"053c55eb-2e39-4a98-9b17-ea8be89f8868","resolution":{"observed_at":"2026-08-04T17:58:14.910102Z","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":"2026-08-04T17:58:14.992217Z","title":"Nonlinear least squares for inverse problems: theoretical foundations and step- by-step guide for applications","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:14.992217Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:dd8bba790ca10e42fb69278fbd22ba3720eb1b9e04d30842cfeaf9b734f7a619","observation_id":"1894bc72-dbe0-4434-8dc8-1c6c79b0b635","resolution":{"observed_at":"2026-08-04T17:58:14.992217Z","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":"2026-08-04T17:58:15.074024Z","title":"Neural symplectic form: Learning hamiltonian equations on general coordinate systems","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:15.074024Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:a19e6f63c79e707eb519a38bdbbbf3d0d74b5cb4d77bcdf7fd6f64c430ad4437","observation_id":"778e7427-59ee-4ab9-bc3e-6f0f76703c76","resolution":{"observed_at":"2026-08-04T17:58:15.074024Z","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":"2026-08-04T17:58:15.129203Z","title":"Group equivariant convolutional networks","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:15.129203Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:07dcdbfd509eb4a428503f83f27654ac2ecd03d14d4aa7361430b32d8f824664","observation_id":"73004122-1c5e-43cc-8436-e966caf6d392","resolution":{"observed_at":"2026-08-04T17:58:15.129203Z","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":"2026-08-04T17:58:15.198642Z","title":"Coverage control for mobile sensing networks","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:15.198642Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:357cc4e62b850c80bd93e01c64bbd9c7f8b8a0aa17474f7ae7409ed8a1ade9d2","observation_id":"d86dc06b-de97-4fb1-9b81-39c206adf0b4","resolution":{"observed_at":"2026-08-04T17:58:15.198642Z","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":"2026-08-04T17:58:15.247352Z","title":"Sinkhorn distances: Lightspeed computation of optimal transport","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:15.247352Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:7c2a5c918f8b43d58acc29995c00594b119f0b8f6067477ab210af0b9edff310","observation_id":"66d0111d-bc54-4f7f-bc1b-9fb4990e3fe1","resolution":{"observed_at":"2026-08-04T17:58:15.247352Z","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":"2026-08-04T17:58:15.328472Z","title":"Deep learning architectures for nonlinear operator functions and nonlinear inverse problems","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:15.328472Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:055aaccf5cf7491b19c6e782b56f3a187feff4f9c10bbc1b4b7aa6914e558366","observation_id":"84ed712a-9d7a-4814-838c-5d975105942b","resolution":{"observed_at":"2026-08-04T17:58:15.328472Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"math/0508341","last_updated":"2005-08-18T22:27:29Z","snapshot_observed_at":"2026-07-07T05:50:41.355296Z","submitted_at":"2005-08-18T14:13:14Z","title":"Discrete Exterior Calculus","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"math/0508341","snapshot_observed_at":"2026-08-04T17:58:15.395145Z","title":"Discrete exterior calculus","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:15.395145Z"},"links":{"cited_paper":"/paper/math/0508341","citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:5cacdc5e9618542c56b6f491aff3d5f7e054bf0e4c570ef1580f48dca8727d49","observation_id":"b8ebb1d5-b347-482a-a893-0058909067f2","resolution":{"observed_at":"2026-08-04T17:58:15.395145Z","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":"2026-08-04T17:58:15.477754Z","title":"Bacterium-inspired robots for environmental monitoring","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:15.477754Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:b3e218c52268215ab6cc3627ab3f432f087366cc52bf3b8f0ce8ae40b2df9289","observation_id":"8bf6a415-05ed-4f15-9c82-49a2a8e04b38","resolution":{"observed_at":"2026-08-04T17:58:15.477754Z","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":"2026-08-04T17:58:15.549101Z","title":"Centroidal voronoi tessellations: Applications and algorithms","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:15.549101Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:0f6dc33d17f74bccb4db165e352d0b208d895c574c54c6dd4220581b56f2bf80","observation_id":"da347469-b619-46b9-bac9-6754134320d6","resolution":{"observed_at":"2026-08-04T17:58:15.549101Z","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":"2026-08-04T17:58:15.614188Z","title":"Regularization of inverse problems","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:15.614188Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:2d2416d822cba2c0398e10ec7bfc50cdd0e488192cab31b27c280e67bc6c34f5","observation_id":"5eb88bc7-ed21-43c3-ba60-31819c19a0d4","resolution":{"observed_at":"2026-08-04T17:58:15.614188Z","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":"2026-08-04T17:58:15.705889Z","title":"Gmsh: A 3-d finite element mesh generator with built-in pre-and post-processing facilities","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:15.705889Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:cb84f7b3f5db1a989086bd473cdf3bddb3e9502c32b0b9ce343c7cb81f1646bc","observation_id":"e42d1431-c9c7-470d-8672-a5239d9c5696","resolution":{"observed_at":"2026-08-04T17:58:15.705889Z","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":"2026-08-04T17:58:15.784033Z","title":"Hamiltonian neural networks","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:15.784033Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:ad2d4f97eb7679a3218168c5e85d810c8c3df015148fe0e63fd9d8c51ddbabe7","observation_id":"d9f807dc-d1bc-4f61-963a-e7dee9eaf737","resolution":{"observed_at":"2026-08-04T17:58:15.784033Z","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":"2026-08-04T17:58:15.843951Z","title":"scikit-fem: A python package for finite element assembly","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:15.843951Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:21ef0653ef695ebb077f9134caea8d2fbfb0467ce3443bca9de040f57b6f3597","observation_id":"a349ecf3-a599-43b0-97a5-7b3e3c54a3d6","resolution":{"observed_at":"2026-08-04T17:58:15.843951Z","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":"2026-08-04T17:58:15.892075Z","title":"Multi-agent search for source localization in a turbulent medium","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:15.892075Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:60f8be2e14d43a9835ba34cf738b263f341f81c6af8297aabf2ba92f22e2a662","observation_id":"98fecafc-93c2-4f7e-9656-f959aa441831","resolution":{"observed_at":"2026-08-04T17:58:15.892075Z","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":"2026-08-04T17:58:15.971481Z","title":"Information theoretic source seeking strate- gies for multiagent plume tracking in turbulent fields","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:15.971481Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:e2814ea3b9b708de3b522556d9063e2df8d562aab58f7b51d9c8dd4c8f2334a2","observation_id":"ca197c97-3f8a-4b2e-ae90-00363f6e6c37","resolution":{"observed_at":"2026-08-04T17:58:15.971481Z","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":"2026-08-04T17:58:16.051647Z","title":"Structure-preserving neural networks","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:16.051647Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:9501fb20403b3b742f45008a964f562fa147dcacac9752321bf99c7507b1d575","observation_id":"6412135b-3d0c-4d25-9229-606e89c76876","resolution":{"observed_at":"2026-08-04T17:58:16.051647Z","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":"2026-08-04T17:58:16.111934Z","title":"Hycom surface velocity fields for the gulf of mexico and the florida straits at 1km resolution for january 2014 and july 2014, 2019","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:16.111934Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:366aa93127fc1262b966a8b95bc79c1873bcd56d3b61f5683102882530bb6d19","observation_id":"d5d4586f-53f3-4c2a-83d2-a101c708aca6","resolution":{"observed_at":"2026-08-04T17:58:16.111934Z","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":"2026-08-04T17:58:16.172242Z","title":"Inverse source problems","venue":null,"work_id":null,"year":1990},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:16.172242Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:903c64845ab351cad0f16c42d4bd62f67a2983c7766aed036af4520012776d6e","observation_id":"93b9b0e2-ea8a-42da-9d7c-ff4a3f582fdf","resolution":{"observed_at":"2026-08-04T17:58:16.172242Z","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":"2026-08-04T17:58:16.232195Z","title":"Physics- informed neural networks for inverse problems in supersonic flows","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:16.232195Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:49e42567fa855dbab55c2ab2b606303a8aa3e2aeac955ce5e147afdce9c6ab00","observation_id":"f4a609c1-0486-4ec4-8166-f57685c34c58","resolution":{"observed_at":"2026-08-04T17:58:16.232195Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.05571","last_updated":"2024-06-08T20:43:50Z","snapshot_observed_at":"2026-08-07T19:05:15.227780Z","submitted_at":"2024-06-08T20:43:50Z","title":"A Structure-Preserving Domain Decomposition Method for Data-Driven Modeling","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.05571","snapshot_observed_at":"2026-08-04T17:58:16.284845Z","title":"A structure-preserving domain decomposition method for data-driven modeling","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:16.284845Z"},"links":{"cited_paper":"/paper/2406.05571","citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:0577f7cec3c19589fcad08cfcd6a48228a88b89d3fbc8a59f4d2df2bcac0ba86","observation_id":"9132b2b7-9416-4b58-93bc-aff69bf5f78c","resolution":{"observed_at":"2026-08-04T17:58:16.284845Z","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":"2026-08-04T17:58:16.368966Z","title":"Deep learning methods for inverse problems","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:16.368966Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:c934d2e54fc6d00e18045f6ac93e77a03aa5b428da070b294239d5332ca03657","observation_id":"66442371-6cd3-4e30-ab95-7066bd5f3371","resolution":{"observed_at":"2026-08-04T17:58:16.368966Z","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":"2026-08-04T17:58:16.423385Z","title":"Physics-informed machine learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:16.423385Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:b1dc4870e61d00c8a061df8c9c685bc35de6a2429a72342b6d25ed88dfbe5385","observation_id":"0f13bc5d-82c5-4074-a24c-63f2d23f7217","resolution":{"observed_at":"2026-08-04T17:58:16.423385Z","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":"2026-08-04T17:58:16.474801Z","title":"Generalized coverage control for time- varying density functions","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:16.474801Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:9500680a71312889ff2bdfcf17c574f529296b0a2d71bdb28b85dd907553f115","observation_id":"4b5315d8-3aea-4cbd-9c92-509422f33cce","resolution":{"observed_at":"2026-08-04T17:58:16.474801Z","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":"2026-08-04T17:58:16.564149Z","title":"Model-based active source identification in complex environments","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:16.564149Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:30b6f4126b21489e53cc93bf645c7832d3b936272215270708ed5c7219362baa","observation_id":"0c7edf8c-0a29-40ff-a3fa-595e3a5232c8","resolution":{"observed_at":"2026-08-04T17:58:16.564149Z","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":"2026-08-04T17:58:16.625758Z","title":"Computing geodesic paths on manifolds","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:16.625758Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:a6131e3296539cab735b7a5619951cbd460860bb660c151426c9d8758802bff8","observation_id":"23f0b2f8-f943-43b8-865a-e8bc9111b2de","resolution":{"observed_at":"2026-08-04T17:58:16.625758Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.06981","last_updated":"2025-08-09T13:26:44Z","snapshot_observed_at":"2026-08-05T22:33:42.566458Z","submitted_at":"2025-08-09T13:26:44Z","title":"Structure-Preserving Digital Twins via Conditional Neural Whitney Forms","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.06981","snapshot_observed_at":"2026-08-04T17:58:16.738129Z","title":"Structure-preserving digital twins via conditional neural whitney forms","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:16.738129Z"},"links":{"cited_paper":"/paper/2508.06981","citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:590b2a39d72cce2bf6b4ba8843e7a9d0ad241f9375bf457bff2028d10d9caeda","observation_id":"4a6b4a3b-87bd-482e-a1fd-602ba41b843d","resolution":{"observed_at":"2026-08-04T17:58:16.738129Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-04T17:58:16.868478Z","title":"Adam: A method for stochastic optimization","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:16.868478Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:e32afa9eab36e0e255cd2d81ddbac4d73c22d15709324b0567b6a50cdc5e6be9","observation_id":"e4fd5906-981b-4d2e-a047-b25cc1562aa1","resolution":{"observed_at":"2026-08-04T17:58:16.868478Z","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":"2026-08-04T17:58:17.004584Z","title":"Machine learning for groundwater pollution source identification and monitoring network optimization","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:17.004584Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:a1de990f67ce5d72b9e133000d47d45c793d6c0e09fe9ccb98eca8ad9f4b1d11","observation_id":"79ada893-f11f-4726-85be-cdcfe1f221c3","resolution":{"observed_at":"2026-08-04T17:58:17.004584Z","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":"2026-08-04T17:58:17.097845Z","title":"Multirobot control using time- varying density functions","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:17.097845Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:822945559d4b0711f53e794ed4ffc6e62998286e0539000911d74cedb55db249","observation_id":"07b7da44-f0ef-4f15-b547-61c1b30726aa","resolution":{"observed_at":"2026-08-04T17:58:17.097845Z","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":"2026-08-04T17:58:17.159861Z","title":"Controlled coverage using time-varying density functions","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:17.159861Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:5562628aea9bf1b267b8903522924ad403441dad82febe9ad54d6073e18072db","observation_id":"f638ce91-7c47-43b0-8258-b0b251d27c27","resolution":{"observed_at":"2026-08-04T17:58:17.159861Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.08895","last_updated":"2021-05-17T03:12:33Z","snapshot_observed_at":"2026-07-06T10:05:26.653366Z","submitted_at":"2020-10-18T00:34:21Z","title":"Fourier Neural Operator for Parametric Partial Differential Equations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.08895","snapshot_observed_at":"2026-08-04T17:58:17.227666Z","title":"Fourier neural operator for parametric partial differential equations","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:17.227666Z"},"links":{"cited_paper":"/paper/2010.08895","citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:2e45c561941dd65cc0e695157a4502d57b6fad9a806b65e06a48a1da061170d5","observation_id":"6f75ad07-349e-4391-aaf5-4e2530992fee","resolution":{"observed_at":"2026-08-04T17:58:17.227666Z","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":"2026-08-04T17:58:17.324396Z","title":"Explainable ai: A review of machine learning interpretability methods","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:17.324396Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:0283747f0787053744c186010b01a18594417bb9f85e7475364e7b9f4739d10b","observation_id":"d0e67d60-fe2c-42e5-b9ff-bdb3baad2f8d","resolution":{"observed_at":"2026-08-04T17:58:17.324396Z","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":"2026-08-04T17:58:17.424495Z","title":"Least squares quantization in pcm","venue":null,"work_id":null,"year":1982},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:17.424495Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:e4aac0a5299d930e125223ddf1d6cc1d6aaadb7fed23dfb4888f0b7f66ec864d","observation_id":"c13ef207-6b97-4e6c-97ea-5f413719d9e5","resolution":{"observed_at":"2026-08-04T17:58:17.424495Z","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":"2026-08-04T17:58:17.568481Z","title":"Whitney forms and their extensions","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:17.568481Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:28bed84010f46a72d449ad131468268ea68d3417d3a560604352cc3f03a4f9f8","observation_id":"a4f85125-b28f-4133-9560-9264277a9223","resolution":{"observed_at":"2026-08-04T17:58:17.568481Z","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":"2026-08-04T17:58:17.663390Z","title":"Learn- ing nonlinear operators via deeponet based on the universal approximation theorem of operators","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:17.663390Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:9ab3fbb527eb5c6998bfee7a58169c01096fc99710606457a6470dc3f67cbb6e","observation_id":"dce1e4e7-2279-4605-87e2-fc7c425876e5","resolution":{"observed_at":"2026-08-04T17:58:17.663390Z","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":"2026-08-04T17:58:17.766861Z","title":"Distributed environmental 27 modeling and adaptive sampling for multi-robot sensor coverage","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:17.766861Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:f98db4d7182a60295b73f1a4098d2232ec3fcb96f2e7b20e48b4fcf5f4b96f2f","observation_id":"07592aee-968a-4f17-ae9a-c8b6b9b119d4","resolution":{"observed_at":"2026-08-04T17:58:17.766861Z","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":"2026-08-04T17:58:17.864635Z","title":"Olfaction-based mobile robot navi- gation","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:17.864635Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:10a5c283181f59937c9a3ed286ddd23fd4099be7160bb503000462240c9aa815","observation_id":"341af6ef-c220-4355-ba7f-7fd739d582bc","resolution":{"observed_at":"2026-08-04T17:58:17.864635Z","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":"2026-08-04T17:58:18.091191Z","title":"Source localization by spatially distributed electronic noses for advection and diffusion","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:18.091191Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:1952d8d7898f6b099828f0fba0bfdffb5a3b5d49666a8d44244f1026a935d9b3","observation_id":"1027e069-2bb7-4d82-b87e-0b4ac1d5726f","resolution":{"observed_at":"2026-08-04T17:58:18.091191Z","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":"2026-08-04T17:58:18.124786Z","title":"Estimates on the generalization error of physics- informed neural networks for approximating a class of inverse problems for pdes","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:18.124786Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:49de644beb34dfeedb0a36e23e5338322fd51dce7f8f025b40cca8c0f628626f","observation_id":"b2ea1551-8078-4b50-aca6-62316baf02c4","resolution":{"observed_at":"2026-08-04T17:58:18.124786Z","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":"2026-08-04T17:58:18.228386Z","title":"The discrete geodesic problem","venue":null,"work_id":null,"year":1987},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:18.228386Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:6209c35724ba9af4ebed19bad1b79afe9c788b77e602b1893b02c9ba3f966874","observation_id":"99ebc821-c6b1-4398-bbc6-e03eb577d4cf","resolution":{"observed_at":"2026-08-04T17:58:18.228386Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.11167","last_updated":"2023-06-03T18:34:05Z","snapshot_observed_at":"2026-07-06T14:44:52.692991Z","submitted_at":"2023-01-26T15:12:58Z","title":"Neural Inverse Operators for Solving PDE Inverse Problems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.11167","snapshot_observed_at":"2026-08-04T17:58:18.298470Z","title":"Neural inverse oper- ators for solving pde inverse problems","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:18.298470Z"},"links":{"cited_paper":"/paper/2301.11167","citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:3d19c588475000d152e8daa2b6aaeddb6094e08b8cfc46185587d27dda6ba805","observation_id":"f56f5369-b64f-4bc7-b3ce-56c9af6d8f78","resolution":{"observed_at":"2026-08-04T17:58:18.298470Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.01703","last_updated":"2019-12-03T22:06:05Z","snapshot_observed_at":"2026-07-06T08:41:49.632205Z","submitted_at":"2019-12-03T22:06:05Z","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.01703","snapshot_observed_at":"2026-08-04T17:58:18.392284Z","title":"Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chil- amkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:18.392284Z"},"links":{"cited_paper":"/paper/1912.01703","citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:a1b81950c527cfb7f870e36d4d60a61748cab991118e7912a14dd1ffb866a098","observation_id":"bcdb2e0f-baa1-4782-a7f0-66d872fd2a39","resolution":{"observed_at":"2026-08-04T17:58:18.392284Z","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":"2026-08-04T17:58:18.547998Z","title":"Thermodynamically consistent physics-informed neural networks for hyperbolic systems","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:18.547998Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:cf1bb5f351c75604ff357056ee054be5e6fedb01ce515fc46f902447d392ed97","observation_id":"248edbdf-b060-42a1-a1e3-6327f29410d5","resolution":{"observed_at":"2026-08-04T17:58:18.547998Z","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":"2026-08-04T17:58:18.646803Z","title":"Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:18.646803Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:545cee4bcdc521c145b1bbd201209080f431c01be84dcb10ff09d6db29ff3fa6","observation_id":"9e8a41f8-936e-46e3-b2ba-0ae8ce6a36f7","resolution":{"observed_at":"2026-08-04T17:58:18.646803Z","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":"2026-08-04T17:58:18.778780Z","title":"A machine learning approach to identifying point source locations in photoacoustic data","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:18.778780Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:f9ef498ef78196962d4f1a247f524be24fdc40d609db97960395af40c44970f5","observation_id":"b3b3688e-41e0-40b4-82bd-e07bb2724d08","resolution":{"observed_at":"2026-08-04T17:58:18.778780Z","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":"2026-08-04T17:58:18.905393Z","title":"Corbijn van Willenswaard","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:18.905393Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:23b0ece5af60651fb3661bd9f696c01c019fb35a7e6e3e1dafa032b745c90da8","observation_id":"c1c9c4fb-4394-4e5e-bebb-786b221eca37","resolution":{"observed_at":"2026-08-04T17:58:18.905393Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.13510","last_updated":"2026-04-22T00:35:04Z","snapshot_observed_at":"2026-07-06T21:26:30.087184Z","submitted_at":"2025-05-16T20:16:14Z","title":"On the definition and importance of interpretability in scientific machine learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.13510","snapshot_observed_at":"2026-08-04T17:58:19.068457Z","title":"On the definition and importance of interpretability in scientific machine learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:19.068457Z"},"links":{"cited_paper":"/paper/2505.13510","citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:f1c0675d42e598239c7d8e264e29a70720831e2b343c34d09e3bd15e9e300fb3","observation_id":"7255cab3-7ec9-4d74-a368-a45f586bebf1","resolution":{"observed_at":"2026-08-04T17:58:19.068457Z","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":"2026-08-04T17:58:19.224341Z","title":"A compar- ison of reactive robot chemotaxis algorithms","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:19.224341Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:cca401141154c0c34632dd98ce6013c689b5a4054a192943718a3c7390d08de0","observation_id":"bf44fab2-15af-48c9-9b05-4a9f7dfbd82b","resolution":{"observed_at":"2026-08-04T17:58:19.224341Z","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":"2026-08-04T17:58:19.357413Z","title":"Decentralized minimum-energy coverage control for time-varying density functions","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:19.357413Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:4f38ed97481a7515553442b88ff7bbfeb501cc8c5aeacfb870dd3c832ec1b039","observation_id":"324153c0-efa2-4fb9-9099-ee84ccfcc513","resolution":{"observed_at":"2026-08-04T17:58:19.357413Z","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":"2026-08-04T17:58:19.511117Z","title":"A distributed formation-based odor source localization algorithm-design, implementation, and wind tun- nel evaluation","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:19.511117Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:ebc02d20cf641b0c8455672bb14af6c46fef28267957fafb000bc8c9a9de12c9","observation_id":"2df18df2-13d1-4514-9cf8-fa337fa6bdbe","resolution":{"observed_at":"2026-08-04T17:58:19.511117Z","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":"2026-08-04T17:58:19.622989Z","title":"Inverse problems: a bayesian perspective","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:19.622989Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:c0a4789f0cfa766ee280d10d9a1edd0a2a302e9ae0005e2b557b0c589c75481f","observation_id":"253f1eee-1132-4c69-b98d-92b150735c40","resolution":{"observed_at":"2026-08-04T17:58:19.622989Z","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":"2026-08-04T17:58:19.804385Z","title":"Inverse problem theory and methods for model parameter estimation","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:19.804385Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:1d540240ae14714bcbd102a84df1e195bb99c662c705a1b26467fe6ed2d7044d","observation_id":"57752cd5-f92f-46aa-b304-ddbfb03cf540","resolution":{"observed_at":"2026-08-04T17:58:19.804385Z","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":"2026-08-04T17:58:19.864182Z","title":"Enforcing exact physics in scientific machine learning: a data-driven exterior calculus on graphs","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:19.864182Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:be74145dc340e04b61d3cab270e45917a5679bab7618649c65c78f64f0ae3afb","observation_id":"28088578-24de-4ccd-9615-6f69b7fa1ed7","resolution":{"observed_at":"2026-08-04T17:58:19.864182Z","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":"2026-08-04T17:58:19.979334Z","title":"Contaminant source identifica- tion using semi-supervised machine learning","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:19.979334Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:d9461f9aeca0e9cd1c43475400564b8d5d39a72d72b34bb9887281b3acf8bd03","observation_id":"45111259-70d4-4851-8680-a04acea692aa","resolution":{"observed_at":"2026-08-04T17:58:19.979334Z","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":"2026-08-04T17:58:20.122055Z","title":"Physics-informed neural network algorithm for solving forward and inverse problems of variable-order space-fractional advection–diffusion equations","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:20.122055Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:948658dd1f43589810b93bbc8bb09cd247d2570eb74c632ab5cde71913eee33c","observation_id":"65774551-a15a-4cd4-a021-3b9a7d2ed9c6","resolution":{"observed_at":"2026-08-04T17:58:20.122055Z","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":"2026-08-04T17:58:20.282404Z","title":"Understanding and mitigating gradient flow pathologies in physics-informed neural networks","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:20.282404Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:0f268730113627a7ebd1b5cc0769da540f3a2c0358cac344ebc92fd09cf15e81","observation_id":"8559b661-dbed-4b13-8cd9-79bfbac41a8e","resolution":{"observed_at":"2026-08-04T17:58:20.282404Z","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":"2026-08-04T17:58:20.399118Z","title":"Bioinspired algorithm for autonomous sensor- driven guidance in turbulent chemical plumes","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:20.399118Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:23b2a250e1cb52df0da52fc83e165021b29c754ecf019de00b545745abb613bc","observation_id":"9095ae02-8615-4826-b05a-bc358ce61f50","resolution":{"observed_at":"2026-08-04T17:58:20.399118Z","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":"2026-08-04T17:58:20.518132Z","title":"Multiple source detection and localization in advection-diffusion processes using wireless sensor networks","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:20.518132Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:2fde3ee2393693aaeef758e0b8c7e820ed98c739e716045da1b2404f292a05dd","observation_id":"aa9528ca-a89b-4853-8064-765ae79bab1b","resolution":{"observed_at":"2026-08-04T17:58:20.518132Z","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":"2026-08-04T17:58:20.618849Z","title":"Foundational research gaps and future directions for digital twins","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:20.618849Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:e4775fef3745b73f4c8342a4061b98c123e10004adc5dd75874a36ae611169fb","observation_id":"36b78367-0fc4-4222-bf41-ea1cb34bfdb5","resolution":{"observed_at":"2026-08-04T17:58:20.618849Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-04T17:58:20.776736Z","title":"Gradient-enhanced physics- informed neural networks for forward and inverse pde problems","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:20.776736Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:5d2c76950d2542e1d456d5c1ab8544ce942834fc7edc5e0025a38dc78ace0d2c","observation_id":"e1db675d-eeea-42fe-b2e1-83af3e3efdaa","resolution":{"observed_at":"2026-08-04T17:58:20.776736Z","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":"2026-08-04T17:58:20.855174Z","title":"Distributed robotics approach to chemical plume tracing","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:20.855174Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:5282e365f3294378276c86a4f579481392ff82253c9ce1f09d02d048cbdc0073","observation_id":"fcb5753e-da41-49c7-9f5b-1d3e49cad9b5","resolution":{"observed_at":"2026-08-04T17:58:20.855174Z","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":"2026-08-04T17:58:20.969532Z","title":"We provide a proof for Theorem 5.4 Proof","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:20.969532Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:a87677e2a761280c914893933cc7ae94ddd441a078a9d1a2495b741bebc8b79d","observation_id":"12d9990c-97b8-4ae3-b1c7-81213ef3d1a1","resolution":{"observed_at":"2026-08-04T17:58:20.969532Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-04T17:58:21.109252Z","title":"Using the simple bounds arctan( θ) ≤ θ and tan(θ) ≤ 2θ, it is straightforward to show (10.17) θ∗ 1 = arctan r′ 1 − r′ tan(θ1) ≤ 2r′ 1 − r′ θ1","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-04T17:58:21.109252Z"},"links":{"citing_paper":"/paper/2509.10363"},"observation_digest":"sha256:b5bdeec032f18a7825046ba99584b7999a0d408c9ff75402a73980072ec76efb","observation_id":"5e75ab68-b5ba-4027-bc73-5a828b386f02","resolution":{"observed_at":"2026-08-04T17:58:21.109252Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2509.10363","last_updated":"2025-09-12T15:54:13Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T17:24:03.466028Z","submitted_at":"2025-09-12T15:54:13Z","title":"Physics-informed sensor coverage through structure preserving machine learning"},"reference_resolution":{"displayed":73,"state_counts":{"malformed_identifier":3,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":70,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":73},"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 73 of 73 outbound references and 2 inbound Pith citation observations for arXiv:2509.10363."}