{"as_of":"2026-08-13T21:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fae89c0ffe4f2b7c70338d97b1012a2da5ece59c7635423072e93196b90cdcb7","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":"2026-08-13T06:32:02.005865+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":"2026-05-10T09:29:22.792337Z","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":"2026-05-10T09:33:41.567320Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2206.06817","last_updated":"2023-11-26T21:59:47Z","snapshot_observed_at":"2026-08-13T15:24:10.121913Z","submitted_at":"2022-06-14T13:11:22Z","title":"Residual-based physics-informed transfer learning: A hybrid method for accelerating long-term CFD simulations via deep learning","version":3},"cited_work":{"arxiv_id":"2206.06817","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.06817","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Lee, J","venue":null,"work_id":"ce7010fa-ee6a-489d-8fea-70e82205920b","year":null},"citing_paper":{"arxiv_id":"2605.05217","last_updated":"2026-04-17T01:48:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-17T01:48:54Z","title":"Physics-Informed Neural Networks with Learnable Loss Balancing and Transfer Learning","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-05-10T09:29:22.792337Z"},"links":{"cited_paper":"/paper/2206.06817","citing_paper":"/paper/2605.05217"},"observation_digest":"sha256:9049408dceb04a63e9eed281c09d5419faf579810e563e59db468857651ae211","observation_id":"5c283bfb-b000-4410-8256-3ae77c906b64","resolution":{"observed_at":"2026-05-10T09:33:41.569863Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2206.06817/citation-record","integrity":"/paper/2206.06817/integrity","json":"/paper/2206.06817/citation-record.json","paper":"/paper/2206.06817"},"outbound":[],"paper":{"arxiv_id":"2206.06817","last_updated":"2023-11-26T21:59:47Z","latest_version":3,"primary_category":"physics.flu-dyn","snapshot_observed_at":"2026-08-13T15:24:10.121913Z","submitted_at":"2022-06-14T13:11:22Z","title":"Residual-based physics-informed transfer learning: A hybrid method for accelerating long-term CFD simulations via deep learning"},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2206.06817."}