{"as_of":"2026-08-09T18:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:159bea4a897d9334651b9406dc5395dbccde9791e18680e4c9d87bd572d0e2a9","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T20:19:21.238037Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T20:19:27.643561Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2104.05584","last_updated":"2022-05-18T09:33:44Z","snapshot_observed_at":"2026-08-08T00:09:23.799370Z","submitted_at":"2021-04-12T15:56:26Z","title":"Physics Informed Neural Networks (PINNs)for approximating nonlinear dispersive PDEs","version":2},"cited_work":{"arxiv_id":"2104.05584","doi":null,"metadata_source":"pith","pith_arxiv_id":"2104.05584","snapshot_observed_at":"2026-08-06T20:19:27.643561Z","title":"Physics Informed Neural Networks (PINNs)for approximating nonlinear dispersive PDEs","venue":"math.NA","work_id":"982eafbe-84a3-4324-8754-07b3189f79a1","year":2021},"citing_paper":{"arxiv_id":"2507.03521","last_updated":"2025-07-09T10:59:30Z","snapshot_observed_at":"2026-08-08T00:09:30.620970Z","submitted_at":"2025-07-04T12:16:57Z","title":"PINN-DG: Residual neural network methods trained with Finite Elements","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T20:19:21.238037Z"},"links":{"cited_paper":"/paper/2104.05584","citing_paper":"/paper/2507.03521"},"observation_digest":"sha256:a78a570a7c04e8f2022445742806ab980a06d340e3f6483556d57bfb4dbd37a2","observation_id":"ecf6c647-3bda-440d-bbc7-329a1cf372a9","resolution":{"observed_at":"2026-08-06T20:19:27.682023Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2104.05584/citation-record","integrity":"/paper/2104.05584/integrity","json":"/paper/2104.05584/citation-record.json","paper":"/paper/2104.05584"},"outbound":[],"paper":{"arxiv_id":"2104.05584","last_updated":"2022-05-18T09:33:44Z","latest_version":2,"primary_category":"math.NA","snapshot_observed_at":"2026-08-08T00:09:23.799370Z","submitted_at":"2021-04-12T15:56:26Z","title":"Physics Informed Neural Networks (PINNs)for approximating nonlinear dispersive PDEs"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2104.05584."}