{"as_of":"2026-08-09T04:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0fb42fdc78fa27f9cb42e4f6f6099c02969ffda10868c125c8991e0ec1219a00","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T12:43:36.469540Z","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":"2026-07-02T00:16:23.881613Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2107.07871","last_updated":"2021-07-16T13:03:47Z","snapshot_observed_at":"2026-07-06T11:29:47.401294Z","submitted_at":"2021-07-16T13:03:47Z","title":"Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.07871","snapshot_observed_at":"2026-08-04T12:43:36.469540Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.02872","last_updated":"2026-06-24T20:09:12Z","snapshot_observed_at":"2026-08-07T19:55:27.634883Z","submitted_at":"2025-10-03T10:18:49Z","title":"A physics-informed neural network approach to the point defect model for electrochemical oxide film growth","version":4},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-04T12:43:36.469540Z"},"links":{"cited_paper":"/paper/2107.07871","citing_paper":"/paper/2510.02872"},"observation_digest":"sha256:0ec0dba5c3f94abdc63eeb6e56062e37b7a13ac7e6eb2c7115566eeec15341fa","observation_id":"6130aeec-ed7b-472c-8308-2069210a03e2","resolution":{"observed_at":"2026-08-04T12:43:36.469540Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.07871","last_updated":"2021-07-16T13:03:47Z","snapshot_observed_at":"2026-07-06T11:29:47.401294Z","submitted_at":"2021-07-16T13:03:47Z","title":"Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations","version":1},"cited_work":{"arxiv_id":"2107.07871","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2107.07871","snapshot_observed_at":"2026-07-02T00:16:23.881613Z","title":"Finite basis physics-informed neural networks (fbpinns): a scalable domain decomposition approach for solving differential equations","venue":null,"work_id":"1799559c-38bd-4069-b412-2ea1d06cdd4f","year":2021},"citing_paper":{"arxiv_id":"2604.23528","last_updated":"2026-04-26T04:30:12Z","snapshot_observed_at":"2026-07-06T23:09:43.659053Z","submitted_at":"2026-04-26T04:30:12Z","title":"When PINNs Go Wrong: Pseudo-Time Stepping Against Spurious Solutions","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-08T06:43:07.836039Z"},"links":{"cited_paper":"/paper/2107.07871","citing_paper":"/paper/2604.23528"},"observation_digest":"sha256:3ee669c15e2922c0fd73b19a69100bca51753f19be0427886e7a55be478f8a23","observation_id":"cf38194b-34db-4c20-b466-388768447ec0","resolution":{"observed_at":"2026-05-11T21:06:14.695237Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.07871","last_updated":"2021-07-16T13:03:47Z","snapshot_observed_at":"2026-07-06T11:29:47.401294Z","submitted_at":"2021-07-16T13:03:47Z","title":"Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations","version":1},"cited_work":{"arxiv_id":"2107.07871","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2107.07871","snapshot_observed_at":"2026-07-02T00:16:23.881613Z","title":"Finite basis physics-informed neural networks (fbpinns): a scalable domain decomposition approach for solving differential equations","venue":null,"work_id":"1799559c-38bd-4069-b412-2ea1d06cdd4f","year":2021},"citing_paper":{"arxiv_id":"2606.02335","last_updated":"2026-06-01T14:44:48Z","snapshot_observed_at":"2026-08-06T09:18:04.025655Z","submitted_at":"2026-06-01T14:44:48Z","title":"Neural Spectral Element Methods for stiff multiphysics PDEs with electrochemical transport benchmarks","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-28T13:33:19.259276Z"},"links":{"cited_paper":"/paper/2107.07871","citing_paper":"/paper/2606.02335"},"observation_digest":"sha256:a11575c1d1246df2e948ffc4f1563af6dda1485c97a76c79a41df2795aa5058b","observation_id":"f73a3efb-505e-4b32-ad61-188804819c7d","resolution":{"observed_at":"2026-07-02T00:16:23.884929Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.07871","last_updated":"2021-07-16T13:03:47Z","snapshot_observed_at":"2026-07-06T11:29:47.401294Z","submitted_at":"2021-07-16T13:03:47Z","title":"Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.07871","snapshot_observed_at":"2026-07-31T02:27:16.920441Z","title":"arXiv e-prints , keywords =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.28604","last_updated":"2026-07-30T17:54:43Z","snapshot_observed_at":"2026-08-06T11:18:05.221988Z","submitted_at":"2026-07-30T17:54:43Z","title":"Cosmo-SPINN: Fuzzy Dark Matter Simulations with Physics-Informed Generative Networks","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-07-31T02:27:16.920441Z"},"links":{"cited_paper":"/paper/2107.07871","citing_paper":"/paper/2607.28604"},"observation_digest":"sha256:e2f069232adc03c5c5803b83563a4e34ebe6aa998d5516fa8f6d596043c32447","observation_id":"659bbee3-c144-4243-b5ab-031cb2eaab29","resolution":{"observed_at":"2026-07-31T02:27:16.920441Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2107.07871/citation-record","integrity":"/paper/2107.07871/integrity","json":"/paper/2107.07871/citation-record.json","paper":"/paper/2107.07871"},"outbound":[],"paper":{"arxiv_id":"2107.07871","last_updated":"2021-07-16T13:03:47Z","latest_version":1,"primary_category":"physics.comp-ph","snapshot_observed_at":"2026-07-06T11:29:47.401294Z","submitted_at":"2021-07-16T13:03:47Z","title":"Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2107.07871."}