{"as_of":"2026-08-11T12:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8ad134df3bede25920bee2444cf3b64d1a726d11173f59609f62da7bbbafed84","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":"2026-08-11T06:34:44.6726+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":"2026-06-28T13:33:19.259276Z","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":"2026-07-04T07:59:40.354075Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2006.16144","last_updated":"2023-12-06T09:20:09Z","snapshot_observed_at":"2026-08-10T04:39:10.828735Z","submitted_at":"2020-06-29T16:05:48Z","title":"Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs","version":3},"cited_work":{"arxiv_id":"2006.16144","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.16144","snapshot_observed_at":"2026-07-04T07:59:40.354075Z","title":"Mishra, R","venue":null,"work_id":"38c65fa3-156d-49a7-a5b8-4b3955025f76","year":2006},"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":6,"source":"pdf_text","source_observed_at":"2026-06-28T13:33:19.259276Z"},"links":{"cited_paper":"/paper/2006.16144","citing_paper":"/paper/2606.02335"},"observation_digest":"sha256:854353c4d47e3b8356981e821af5b91ae5e7d4792f686a201ea66759d890d8f2","observation_id":"61679595-1ce7-45b5-b99b-984607e0e648","resolution":{"observed_at":"2026-07-02T00:16:23.935072Z","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":"2006.16144","last_updated":"2023-12-06T09:20:09Z","snapshot_observed_at":"2026-08-10T04:39:10.828735Z","submitted_at":"2020-06-29T16:05:48Z","title":"Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs","version":3},"cited_work":{"arxiv_id":"2006.16144","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.16144","snapshot_observed_at":"2026-07-04T07:59:40.354075Z","title":"Mishra, R","venue":null,"work_id":"38c65fa3-156d-49a7-a5b8-4b3955025f76","year":2006},"citing_paper":{"arxiv_id":"2606.22150","last_updated":"2026-06-20T17:02:30Z","snapshot_observed_at":"2026-07-06T23:57:04.096779Z","submitted_at":"2026-06-20T17:02:30Z","title":"Parameterized Representations via Implicit Stochastic Modulation for High-Dimensional and High-Order Neural PDE Solvers","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-26T12:20:25.523371Z"},"links":{"cited_paper":"/paper/2006.16144","citing_paper":"/paper/2606.22150"},"observation_digest":"sha256:9257da87d14f2c60a8ec432ccbbbbc98ae3694bdda47126b60fbb6579c7816fc","observation_id":"f3e2e0cb-ac87-42c3-9e2b-a1dde9be9599","resolution":{"observed_at":"2026-07-04T07:59:40.355275Z","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/2006.16144/citation-record","integrity":"/paper/2006.16144/integrity","json":"/paper/2006.16144/citation-record.json","paper":"/paper/2006.16144"},"outbound":[],"paper":{"arxiv_id":"2006.16144","last_updated":"2023-12-06T09:20:09Z","latest_version":3,"primary_category":"math.NA","snapshot_observed_at":"2026-08-10T04:39:10.828735Z","submitted_at":"2020-06-29T16:05:48Z","title":"Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs"},"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 2 inbound Pith citation observations for arXiv:2006.16144."}