{"as_of":"2026-08-10T20:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:de639d8b45e75c309c588f07080440a8b6d3eafd29e213865f27d83a8301bc2f","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":"2026-08-10T06:31:04.303077+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":"2026-08-10T17:38:24.719842Z","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":"2026-07-09T12:56:14.900613Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2205.13748","last_updated":"2023-11-27T04:41:51Z","snapshot_observed_at":"2026-08-10T20:10:19.767419Z","submitted_at":"2022-05-27T03:24:31Z","title":"Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.13748","snapshot_observed_at":"2026-08-10T17:38:24.719842Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.12053","last_updated":"2025-01-21T11:26:02Z","snapshot_observed_at":"2026-08-10T17:31:03.081177Z","submitted_at":"2025-01-21T11:26:02Z","title":"PINNsAgent: Automated PDE Surrogation with Large Language Models","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-10T17:38:24.719842Z"},"links":{"cited_paper":"/paper/2205.13748","citing_paper":"/paper/2501.12053"},"observation_digest":"sha256:876c1c701a412009e84cd5e2e1deadd656c90230ff934497835a28d1b555cbeb","observation_id":"8f761cbb-fc5a-479d-948b-5a6182895f86","resolution":{"observed_at":"2026-08-10T17:38:24.719842Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.13748","last_updated":"2023-11-27T04:41:51Z","snapshot_observed_at":"2026-08-10T20:10:19.767419Z","submitted_at":"2022-05-27T03:24:31Z","title":"Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.13748","snapshot_observed_at":"2026-08-04T21:21:40.962626Z","title":"Auto-pinn: understanding and optimizing physics-informed neural architecture","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-09T18:33:14.176325Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:40.962626Z"},"links":{"cited_paper":"/paper/2205.13748","citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:7e6485ebf7ee390e3b41a7ff72146065411f29870b116d419083a623ed5380da","observation_id":"8fbf4031-fe8e-49c5-8b4b-f7e869f33e96","resolution":{"observed_at":"2026-08-04T21:21:40.962626Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.13748","last_updated":"2023-11-27T04:41:51Z","snapshot_observed_at":"2026-08-10T20:10:19.767419Z","submitted_at":"2022-05-27T03:24:31Z","title":"Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture","version":2},"cited_work":{"arxiv_id":"2205.13748","doi":null,"metadata_source":"pith","pith_arxiv_id":"2205.13748","snapshot_observed_at":"2026-07-09T12:56:14.900613Z","title":"and Hu, X","venue":"cs.LG","work_id":"68deca19-f02d-4e38-8557-b0f14f0e5006","year":2022},"citing_paper":{"arxiv_id":"2601.03613","last_updated":"2026-05-20T16:35:35Z","snapshot_observed_at":"2026-08-02T19:27:53.235804Z","submitted_at":"2026-01-07T05:41:50Z","title":"A Simple but Efficient Transformer-Based Physics-Informed Neural Network for Incompressible Navier--Stokes Equations","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-21T17:04:21.164340Z"},"links":{"cited_paper":"/paper/2205.13748","citing_paper":"/paper/2601.03613"},"observation_digest":"sha256:5febeac79309de6ef855cd9fba135ac373aa38c8f80fcd5e801cc95b0a3872e2","observation_id":"2132007a-38c0-4bb0-89fa-7ed95e9a6f62","resolution":{"observed_at":"2026-05-21T17:05:24.270171Z","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":"2205.13748","last_updated":"2023-11-27T04:41:51Z","snapshot_observed_at":"2026-08-10T20:10:19.767419Z","submitted_at":"2022-05-27T03:24:31Z","title":"Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture","version":2},"cited_work":{"arxiv_id":"2205.13748","doi":null,"metadata_source":"pith","pith_arxiv_id":"2205.13748","snapshot_observed_at":"2026-07-09T12:56:14.900613Z","title":"and Hu, X","venue":"cs.LG","work_id":"68deca19-f02d-4e38-8557-b0f14f0e5006","year":2022},"citing_paper":{"arxiv_id":"2607.07379","last_updated":"2026-07-08T13:10:35Z","snapshot_observed_at":"2026-08-07T01:00:27.976967Z","submitted_at":"2026-07-08T13:10:35Z","title":"Physics-Audited Agentic Discovery in Scientific Machine Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-09T12:49:30.003524Z"},"links":{"cited_paper":"/paper/2205.13748","citing_paper":"/paper/2607.07379"},"observation_digest":"sha256:e1bfecf37f417d18497ba44796e06437f37cd66a8ba5712a8c05aea7ea39a4ab","observation_id":"e0287d7c-4c0c-4b63-b969-f0e0eff96efc","resolution":{"observed_at":"2026-07-09T12:56:14.902611Z","resolver_source":"local_arxiv","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"}}],"links":{"evidence":"/evidence","html":"/paper/2205.13748/citation-record","integrity":"/paper/2205.13748/integrity","json":"/paper/2205.13748/citation-record.json","paper":"/paper/2205.13748"},"outbound":[],"paper":{"arxiv_id":"2205.13748","last_updated":"2023-11-27T04:41:51Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T20:10:19.767419Z","submitted_at":"2022-05-27T03:24:31Z","title":"Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture"},"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 4 inbound Pith citation observations for arXiv:2205.13748."}