{"as_of":"2026-08-19T17:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9de7a35674241810d21c6fbc472f0fa90e4ad689c691fb29194b08a3a03ef224","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+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":"2026-08-06T20:49:35.566552Z","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":"2026-08-06T20:49:37.573225Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2202.01943","last_updated":"2022-10-20T22:23:25Z","snapshot_observed_at":"2026-08-16T17:23:43.129265Z","submitted_at":"2022-02-04T02:21:31Z","title":"PSO-PINN: Physics-Informed Neural Networks Trained with Particle Swarm Optimization","version":3},"cited_work":{"arxiv_id":"2202.01943","doi":null,"metadata_source":"pith","pith_arxiv_id":"2202.01943","snapshot_observed_at":"2026-08-06T20:49:37.573225Z","title":"PSO-PINN: Physics-Informed Neural Networks Trained with Particle Swarm Optimization","venue":"cs.LG","work_id":"ff09aa18-cb01-44b9-a0e7-03a750ec8b8b","year":2022},"citing_paper":{"arxiv_id":"2507.01714","last_updated":"2025-07-02T13:44:31Z","snapshot_observed_at":"2026-08-12T01:55:38.798544Z","submitted_at":"2025-07-02T13:44:31Z","title":"B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T20:49:35.566552Z"},"links":{"cited_paper":"/paper/2202.01943","citing_paper":"/paper/2507.01714"},"observation_digest":"sha256:f8a6fca68e15e40cb8c8c112be641df4d25bde4d979900d5b9da66f43a82c98b","observation_id":"0c760541-3443-49a1-87ea-9dfb77f5da48","resolution":{"observed_at":"2026-08-06T20:49:37.619307Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.01943","last_updated":"2022-10-20T22:23:25Z","snapshot_observed_at":"2026-08-16T17:23:43.129265Z","submitted_at":"2022-02-04T02:21:31Z","title":"PSO-PINN: Physics-Informed Neural Networks Trained with Particle Swarm Optimization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.01943","snapshot_observed_at":"2026-08-01T06:09:01.344971Z","title":"Pso-pinn: Physics-informed neural networks trained with particle swarm optimization.arXiv preprint arXiv:2202.01943,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22004","last_updated":"2026-07-24T06:07:14Z","snapshot_observed_at":"2026-08-17T22:16:48.205416Z","submitted_at":"2026-07-24T06:07:14Z","title":"Energy Manifold Natural Gradient Descent: Riemannian Optimization for Neural PDE Solvers","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-01T06:09:01.344971Z"},"links":{"cited_paper":"/paper/2202.01943","citing_paper":"/paper/2607.22004"},"observation_digest":"sha256:b4af268f2f48f660f9f14491c6d48041c973e89a21417cc30d180a44f1eb23d8","observation_id":"be03a5c2-b598-46b2-8ba8-9fd89a12b5ef","resolution":{"observed_at":"2026-08-01T06:09:01.344971Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2202.01943/citation-record","integrity":"/paper/2202.01943/integrity","json":"/paper/2202.01943/citation-record.json","paper":"/paper/2202.01943"},"outbound":[],"paper":{"arxiv_id":"2202.01943","last_updated":"2022-10-20T22:23:25Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T17:23:43.129265Z","submitted_at":"2022-02-04T02:21:31Z","title":"PSO-PINN: Physics-Informed Neural Networks Trained with Particle Swarm Optimization"},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2202.01943."}