{"as_of":"2026-08-11T16:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1ac669b76f5b83e7731a1376323d757f02eadbe370def1b698ca0859b1e83459","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:28:01.504616Z","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":"2026-07-03T10:58:03.002973Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2111.01394","last_updated":"2021-11-02T06:39:54Z","snapshot_observed_at":"2026-08-11T16:15:22.094286Z","submitted_at":"2021-11-02T06:39:54Z","title":"Solving Partial Differential Equations with Point Source Based on Physics-Informed Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.01394","snapshot_observed_at":"2026-08-06T23:28:01.504616Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18018","last_updated":"2025-06-22T12:55:22Z","snapshot_observed_at":"2026-08-11T03:35:54.090763Z","submitted_at":"2025-06-22T12:55:22Z","title":"A deep-learning model for predicting daily PM2.5 concentration in response to emission reduction","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T23:28:01.504616Z"},"links":{"cited_paper":"/paper/2111.01394","citing_paper":"/paper/2506.18018"},"observation_digest":"sha256:e9544f485948dd37b891d57edc629331b1e64b535284b42e7c6f3c25049a8201","observation_id":"c659f605-f708-4abd-91f7-ab8c07b0abb2","resolution":{"observed_at":"2026-08-06T23:28:01.504616Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.01394","last_updated":"2021-11-02T06:39:54Z","snapshot_observed_at":"2026-08-11T16:15:22.094286Z","submitted_at":"2021-11-02T06:39:54Z","title":"Solving Partial Differential Equations with Point Source Based on Physics-Informed Neural Networks","version":1},"cited_work":{"arxiv_id":"2111.01394","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2111.01394","snapshot_observed_at":"2026-07-03T10:58:03.002973Z","title":"Solving partial differential equations with point source based on physics-informed neural networks","venue":null,"work_id":"2aa7e687-8ec0-429e-a0e1-274e2c7debde","year":2021},"citing_paper":{"arxiv_id":"2605.08915","last_updated":"2026-05-09T12:29:10Z","snapshot_observed_at":"2026-07-06T23:21:06.773761Z","submitted_at":"2026-05-09T12:29:10Z","title":"Physics-Informed Neural PDE Solvers via Spatio-Temporal MeanFlow","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-05-12T03:54:07.625986Z"},"links":{"cited_paper":"/paper/2111.01394","citing_paper":"/paper/2605.08915"},"observation_digest":"sha256:57a577fec15f54fa24e033aa1fb9ee6f17b363ac053c78cb6df62797cae5b56c","observation_id":"2aa26686-f681-40cc-85f4-633583002791","resolution":{"observed_at":"2026-05-12T06:51:28.330344Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.01394","last_updated":"2021-11-02T06:39:54Z","snapshot_observed_at":"2026-08-11T16:15:22.094286Z","submitted_at":"2021-11-02T06:39:54Z","title":"Solving Partial Differential Equations with Point Source Based on Physics-Informed Neural Networks","version":1},"cited_work":{"arxiv_id":"2111.01394","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2111.01394","snapshot_observed_at":"2026-07-03T10:58:03.002973Z","title":"Solving partial differential equations with point source based on physics-informed neural networks","venue":null,"work_id":"2aa7e687-8ec0-429e-a0e1-274e2c7debde","year":2021},"citing_paper":{"arxiv_id":"2606.12735","last_updated":"2026-06-10T22:53:43Z","snapshot_observed_at":"2026-08-05T00:05:05.358595Z","submitted_at":"2026-06-10T22:53:43Z","title":"Physics-Informed Neural Networks and Radial Basis Functions for PDEs with Dirac Delta Sources","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-27T09:46:27.863379Z"},"links":{"cited_paper":"/paper/2111.01394","citing_paper":"/paper/2606.12735"},"observation_digest":"sha256:25182e251953ae9dda889e7113485d8e410d9f3282683b82496f50bacc666464","observation_id":"140db232-cb23-447a-831c-588bb3f063be","resolution":{"observed_at":"2026-07-03T10:58:03.005578Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2111.01394/citation-record","integrity":"/paper/2111.01394/integrity","json":"/paper/2111.01394/citation-record.json","paper":"/paper/2111.01394"},"outbound":[],"paper":{"arxiv_id":"2111.01394","last_updated":"2021-11-02T06:39:54Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-11T16:15:22.094286Z","submitted_at":"2021-11-02T06:39:54Z","title":"Solving Partial Differential Equations with Point Source Based on Physics-Informed Neural Networks"},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2111.01394."}