{"as_of":"2026-08-08T19:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:796a3e514eccb33f4d05e015891e70aba473814852165d2d647a4e9144f7ebca","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":"2026-08-08T06:32:00.761636+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":"2026-08-07T10:48:10.354962Z","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":"2026-08-05T22:40:13.452581Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2201.08690","last_updated":"2022-01-15T05:52:38Z","snapshot_observed_at":"2026-08-07T21:14:14.294382Z","submitted_at":"2022-01-15T05:52:38Z","title":"A deep learning energy method for hyperelasticity and viscoelasticity","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.08690","snapshot_observed_at":"2026-08-07T10:48:10.354962Z","title":"Abueidda, Seid Koric, Rashid Abu Al-Rub, Corey M","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.04375","last_updated":"2025-06-04T18:45:27Z","snapshot_observed_at":"2026-08-08T11:59:27.630253Z","submitted_at":"2025-06-04T18:45:27Z","title":"Solving engineering eigenvalue problems with neural networks using the Rayleigh quotient","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T10:48:10.354962Z"},"links":{"cited_paper":"/paper/2201.08690","citing_paper":"/paper/2506.04375"},"observation_digest":"sha256:0d0df85e2f4d77fac8b39680acaf5b98600562adb8d527df3967d9779983d192","observation_id":"8ce2eb44-95b7-4b3d-bdcc-b917f09d8d8e","resolution":{"observed_at":"2026-08-07T10:48:10.354962Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.08690","last_updated":"2022-01-15T05:52:38Z","snapshot_observed_at":"2026-08-07T21:14:14.294382Z","submitted_at":"2022-01-15T05:52:38Z","title":"A deep learning energy method for hyperelasticity and viscoelasticity","version":1},"cited_work":{"arxiv_id":"2201.08690","doi":null,"metadata_source":"pith","pith_arxiv_id":"2201.08690","snapshot_observed_at":"2026-08-05T22:40:13.452581Z","title":"A deep learning energy method for hyperelasticity and viscoelasticity","venue":"cs.LG","work_id":"f924a9c0-b29c-47f5-8163-7a2d99f4e816","year":2022},"citing_paper":{"arxiv_id":"2508.08309","last_updated":"2025-08-08T19:20:39Z","snapshot_observed_at":"2026-08-07T21:13:17.876061Z","submitted_at":"2025-08-08T19:20:39Z","title":"Variational volume reconstruction with the Deep Ritz Method","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-05T22:40:09.097985Z"},"links":{"cited_paper":"/paper/2201.08690","citing_paper":"/paper/2508.08309"},"observation_digest":"sha256:27d97e3ff7d83a4f44866418ddc140c4f791cc6627c5702e2a30a46ff27d2cc6","observation_id":"f85a2de9-37ff-4cfa-bd55-3ac407a33d2c","resolution":{"observed_at":"2026-08-05T22:40:13.457129Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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"}}],"links":{"evidence":"/evidence","html":"/paper/2201.08690/citation-record","integrity":"/paper/2201.08690/integrity","json":"/paper/2201.08690/citation-record.json","paper":"/paper/2201.08690"},"outbound":[],"paper":{"arxiv_id":"2201.08690","last_updated":"2022-01-15T05:52:38Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T21:14:14.294382Z","submitted_at":"2022-01-15T05:52:38Z","title":"A deep learning energy method for hyperelasticity and viscoelasticity"},"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 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2201.08690."}