{"as_of":"2026-08-21T17:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b48e9bd501381739c042550dd89d3117aafe885afd4e6b96bf5e2ff7baf07fd2","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":"2026-08-21T06:32:19.484+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":"2026-08-01T13:21:46.018331Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2504.09804","last_updated":"2025-04-14T02:07:45Z","snapshot_observed_at":"2026-08-16T12:41:31.948753Z","submitted_at":"2025-04-14T02:07:45Z","title":"BO-SA-PINNs: Self-adaptive physics-informed neural networks based on Bayesian optimization for automatically designing PDE solvers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.09804","snapshot_observed_at":"2026-08-01T13:21:46.018331Z","title":"arXiv preprint arXiv:2504.09804 , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19167","last_updated":"2026-07-21T15:03:42Z","snapshot_observed_at":"2026-08-16T17:52:37.404844Z","submitted_at":"2026-07-21T15:03:42Z","title":"Boundary-Adapted PINNs for Elliptic Dirichlet Problems: $H^2(\\Omega)$ A Priori Error Bounds with Application to Mean Escape Time Computation","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-01T13:21:46.018331Z"},"links":{"cited_paper":"/paper/2504.09804","citing_paper":"/paper/2607.19167"},"observation_digest":"sha256:ea9ad6ae45298904676b0803906263b4b107d79bad831a9958c8fdba1fcbf35b","observation_id":"f2d868d7-413b-435a-b502-13f939d19775","resolution":{"observed_at":"2026-08-01T13:21:46.018331Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2504.09804/citation-record","integrity":"/paper/2504.09804/integrity","json":"/paper/2504.09804/citation-record.json","paper":"/paper/2504.09804"},"outbound":[],"paper":{"arxiv_id":"2504.09804","last_updated":"2025-04-14T02:07:45Z","latest_version":1,"primary_category":"cs.CE","snapshot_observed_at":"2026-08-16T12:41:31.948753Z","submitted_at":"2025-04-14T02:07:45Z","title":"BO-SA-PINNs: Self-adaptive physics-informed neural networks based on Bayesian optimization for automatically designing PDE solvers"},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2504.09804."}