{"as_of":"2026-08-10T17:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8fe011ec003be58a45482024a30ba549ccaa96850243e72d20ea67a10f72e08c","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T16:40:20.131830Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":5,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.18240","last_updated":"2024-11-27T11:27:18Z","snapshot_observed_at":"2026-07-06T19:57:51.125083Z","submitted_at":"2024-11-27T11:27:18Z","title":"Physics Informed Neural Networks (PINNs) as intelligent computing technique for solving partial differential equations: Limitation and Future prospects","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.18240","snapshot_observed_at":"2026-08-06T16:40:20.131830Z","title":"Zhang, W","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.12941","last_updated":"2026-07-10T06:47:47Z","snapshot_observed_at":"2026-08-06T16:31:59.298037Z","submitted_at":"2025-07-17T09:29:22Z","title":"Adaptive feature capture method for solving partial differential equations with near singular solutions","version":4},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T16:40:20.131830Z"},"links":{"cited_paper":"/paper/2411.18240","citing_paper":"/paper/2507.12941"},"observation_digest":"sha256:a827f534d78aba8fec65896a261d74bf02219102866e7a0dc14da05f64844e1b","observation_id":"7dcf7f32-5ff4-4933-baf1-8e6227af0181","resolution":{"observed_at":"2026-08-06T16:40:20.131830Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.18240","last_updated":"2024-11-27T11:27:18Z","snapshot_observed_at":"2026-07-06T19:57:51.125083Z","submitted_at":"2024-11-27T11:27:18Z","title":"Physics Informed Neural Networks (PINNs) as intelligent computing technique for solving partial differential equations: Limitation and Future prospects","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.18240","snapshot_observed_at":"2026-08-06T05:15:42.081951Z","title":"Zhang , author W","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.02166","last_updated":"2025-08-04T08:06:37Z","snapshot_observed_at":"2026-08-06T05:13:10.073253Z","submitted_at":"2025-08-04T08:06:37Z","title":"Physics-informed Fourier Basis Neural Network for Fluid Mechanics","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-06T05:15:42.081951Z"},"links":{"cited_paper":"/paper/2411.18240","citing_paper":"/paper/2508.02166"},"observation_digest":"sha256:da32ce94dd25eda926ade6d3f2387a5702bd8a77380228d1fbec09fb71842952","observation_id":"6811c49b-20d8-4846-98dc-dd1515517ea3","resolution":{"observed_at":"2026-08-06T05:15:42.081951Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.18240","last_updated":"2024-11-27T11:27:18Z","snapshot_observed_at":"2026-07-06T19:57:51.125083Z","submitted_at":"2024-11-27T11:27:18Z","title":"Physics Informed Neural Networks (PINNs) as intelligent computing technique for solving partial differential equations: Limitation and Future prospects","version":1},"cited_work":{"arxiv_id":"2411.18240","doi":"10.48550/arxiv.2411.18240","metadata_source":"arxiv_reference","pith_arxiv_id":"2411.18240","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Zhang, W","venue":"arXiv (Cornell University)","work_id":"63fc51e3-e6fe-42e0-82d7-56799f78cf1d","year":2024},"citing_paper":{"arxiv_id":"2604.28180","last_updated":"2026-04-30T17:57:22Z","snapshot_observed_at":"2026-08-03T03:27:59.829955Z","submitted_at":"2026-04-30T17:57:22Z","title":"An adaptive wavelet-based PINN for problems with localized high-magnitude source","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-07T07:14:05.096056Z"},"links":{"cited_paper":"/paper/2411.18240","citing_paper":"/paper/2604.28180"},"observation_digest":"sha256:f33578f32c2a021c867d66e061698c5d1450ccc3ec65162799dc419ce61d64f9","observation_id":"f8e5a913-a718-4d66-8d02-a0099da47df4","resolution":{"observed_at":"2026-05-09T04:50:11.691731Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.18240","last_updated":"2024-11-27T11:27:18Z","snapshot_observed_at":"2026-07-06T19:57:51.125083Z","submitted_at":"2024-11-27T11:27:18Z","title":"Physics Informed Neural Networks (PINNs) as intelligent computing technique for solving partial differential equations: Limitation and Future prospects","version":1},"cited_work":{"arxiv_id":"2411.18240","doi":"10.48550/arxiv.2411.18240","metadata_source":"arxiv_reference","pith_arxiv_id":"2411.18240","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Zhang, W","venue":"arXiv (Cornell University)","work_id":"63fc51e3-e6fe-42e0-82d7-56799f78cf1d","year":2024},"citing_paper":{"arxiv_id":"2605.16078","last_updated":"2026-05-15T15:39:52Z","snapshot_observed_at":"2026-08-02T06:29:20.591830Z","submitted_at":"2026-05-15T15:39:52Z","title":"A numerical study into neural network surrogate model performance for uncertainty propagation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-19T18:57:56.373765Z"},"links":{"cited_paper":"/paper/2411.18240","citing_paper":"/paper/2605.16078"},"observation_digest":"sha256:15af596139b99c62d4712ef81ad98a59736bb1654ddcff9a07eb4fa6ec8f2b41","observation_id":"bcfe0714-f427-4926-b130-9a67e33b6805","resolution":{"observed_at":"2026-05-19T19:02:43.611576Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.18240","last_updated":"2024-11-27T11:27:18Z","snapshot_observed_at":"2026-07-06T19:57:51.125083Z","submitted_at":"2024-11-27T11:27:18Z","title":"Physics Informed Neural Networks (PINNs) as intelligent computing technique for solving partial differential equations: Limitation and Future prospects","version":1},"cited_work":{"arxiv_id":"2411.18240","doi":"10.48550/arxiv.2411.18240","metadata_source":"arxiv_reference","pith_arxiv_id":"2411.18240","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Zhang, W","venue":"arXiv (Cornell University)","work_id":"63fc51e3-e6fe-42e0-82d7-56799f78cf1d","year":2024},"citing_paper":{"arxiv_id":"2606.23435","last_updated":"2026-06-22T14:55:38Z","snapshot_observed_at":"2026-07-06T23:58:11.694332Z","submitted_at":"2026-06-22T14:55:38Z","title":"Bayesian Analysis Using a Constrained Mixture of Normal-Inverse-Gamma Models","version":1},"reference_index":172,"source":"arxiv_source","source_observed_at":"2026-06-26T07:35:00.683580Z"},"links":{"cited_paper":"/paper/2411.18240","citing_paper":"/paper/2606.23435"},"observation_digest":"sha256:80b81e440dceb821194b8ffeaa41007cdc635189b47ae5c7927126bfe5b4b306","observation_id":"b32b4643-0d7b-4ee2-8f82-82ac1e8b9b1a","resolution":{"observed_at":"2026-07-04T11:49:50.830567Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.18240","last_updated":"2024-11-27T11:27:18Z","snapshot_observed_at":"2026-07-06T19:57:51.125083Z","submitted_at":"2024-11-27T11:27:18Z","title":"Physics Informed Neural Networks (PINNs) as intelligent computing technique for solving partial differential equations: Limitation and Future prospects","version":1},"cited_work":{"arxiv_id":"2411.18240","doi":"10.48550/arxiv.2411.18240","metadata_source":"arxiv_reference","pith_arxiv_id":"2411.18240","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Zhang, W","venue":"arXiv (Cornell University)","work_id":"63fc51e3-e6fe-42e0-82d7-56799f78cf1d","year":2024},"citing_paper":{"arxiv_id":"2606.24696","last_updated":"2026-06-23T15:24:05Z","snapshot_observed_at":"2026-08-06T20:09:32.635453Z","submitted_at":"2026-06-23T15:24:05Z","title":"A Physics-Informed Fourier-Wavelet Transformer for Multiscale Computational Fluid Dynamics Surrogate Modeling","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-25T22:47:05.775339Z"},"links":{"cited_paper":"/paper/2411.18240","citing_paper":"/paper/2606.24696"},"observation_digest":"sha256:8abb2241c01ce4bc52e020389a7aa380fd3eb29257a5bf17b1d47e84039e7e94","observation_id":"71a04c13-e20d-47db-afba-feb90da11e49","resolution":{"observed_at":"2026-07-04T18:30:02.725426Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.18240/citation-record","integrity":"/paper/2411.18240/integrity","json":"/paper/2411.18240/citation-record.json","paper":"/paper/2411.18240"},"outbound":[],"paper":{"arxiv_id":"2411.18240","last_updated":"2024-11-27T11:27:18Z","latest_version":1,"primary_category":"physics.comp-ph","snapshot_observed_at":"2026-07-06T19:57:51.125083Z","submitted_at":"2024-11-27T11:27:18Z","title":"Physics Informed Neural Networks (PINNs) as intelligent computing technique for solving partial differential equations: Limitation and Future prospects"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2411.18240."}