{"as_of":"2026-08-09T02:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7c931300e0ca25895588af27365b0b34dcb47bf015484bd703684d2d71ac37a8","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":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T10:13:41.463768Z","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":"2026-07-02T02:06:27.733905Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2308.05732","last_updated":"2023-10-21T15:41:47Z","snapshot_observed_at":"2026-07-06T16:04:58.235073Z","submitted_at":"2023-08-10T17:53:05Z","title":"PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers","version":2},"cited_work":{"arxiv_id":"2308.05732","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2308.05732","snapshot_observed_at":"2026-07-02T02:06:27.733905Z","title":"Veeling, Paris Perdikaris, Richard E","venue":null,"work_id":"2d0690ba-4a99-441e-a401-83b45a0ef7b2","year":2023},"citing_paper":{"arxiv_id":"2506.07969","last_updated":"2026-04-19T19:45:38Z","snapshot_observed_at":"2026-07-06T21:39:13.304260Z","submitted_at":"2025-06-09T17:44:20Z","title":"A Two-Phase Deep Learning Framework for Adaptive Time-Stepping in High-Speed Flow Modeling","version":2},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-05-19T10:17:34.190090Z"},"links":{"cited_paper":"/paper/2308.05732","citing_paper":"/paper/2506.07969"},"observation_digest":"sha256:084288e4a39e3664cfe10b8124fb31d05d47129bb0fea1873967f8e31fb61a48","observation_id":"c77851f6-881a-42f2-909c-77a35c412848","resolution":{"observed_at":"2026-05-19T10:22:14.692978Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2308.05732","last_updated":"2023-10-21T15:41:47Z","snapshot_observed_at":"2026-07-06T16:04:58.235073Z","submitted_at":"2023-08-10T17:53:05Z","title":"PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers","version":2},"cited_work":{"arxiv_id":"2308.05732","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2308.05732","snapshot_observed_at":"2026-07-02T02:06:27.733905Z","title":"Veeling, Paris Perdikaris, Richard E","venue":null,"work_id":"2d0690ba-4a99-441e-a401-83b45a0ef7b2","year":2023},"citing_paper":{"arxiv_id":"2602.11229","last_updated":"2026-05-06T20:12:38Z","snapshot_observed_at":"2026-07-06T22:45:29.609382Z","submitted_at":"2026-02-11T15:34:52Z","title":"Latent Generative Solvers for Generalizable Long-Term Physics Simulation","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-16T05:21:13.280286Z"},"links":{"cited_paper":"/paper/2308.05732","citing_paper":"/paper/2602.11229"},"observation_digest":"sha256:386aee7d50fd4159531ee9766591dcaf4c8397cfc8d0607e8fbaf4ab75ac0865","observation_id":"7d491d3a-914f-45cc-9ea1-2842aad391e6","resolution":{"observed_at":"2026-05-16T05:22:22.576431Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2308.05732","last_updated":"2023-10-21T15:41:47Z","snapshot_observed_at":"2026-07-06T16:04:58.235073Z","submitted_at":"2023-08-10T17:53:05Z","title":"PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers","version":2},"cited_work":{"arxiv_id":"2308.05732","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2308.05732","snapshot_observed_at":"2026-07-02T02:06:27.733905Z","title":"Veeling, Paris Perdikaris, Richard E","venue":null,"work_id":"2d0690ba-4a99-441e-a401-83b45a0ef7b2","year":2023},"citing_paper":{"arxiv_id":"2604.16721","last_updated":"2026-04-17T21:52:37Z","snapshot_observed_at":"2026-07-06T23:03:57.199731Z","submitted_at":"2026-04-17T21:52:37Z","title":"Late Fusion Neural Operators for Extrapolation Across Parameter Space in Partial Differential Equations","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-10T08:11:08.769850Z"},"links":{"cited_paper":"/paper/2308.05732","citing_paper":"/paper/2604.16721"},"observation_digest":"sha256:5e923a3085471438d6f431be89c4c83af1349e408bbf68d3db96a1772ceea40f","observation_id":"c10aa05c-c3bd-4033-b656-28139af4e24c","resolution":{"observed_at":"2026-05-10T08:12:26.539032Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2308.05732","last_updated":"2023-10-21T15:41:47Z","snapshot_observed_at":"2026-07-06T16:04:58.235073Z","submitted_at":"2023-08-10T17:53:05Z","title":"PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers","version":2},"cited_work":{"arxiv_id":"2308.05732","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2308.05732","snapshot_observed_at":"2026-07-02T02:06:27.733905Z","title":"Veeling, Paris Perdikaris, Richard E","venue":null,"work_id":"2d0690ba-4a99-441e-a401-83b45a0ef7b2","year":2023},"citing_paper":{"arxiv_id":"2605.16818","last_updated":"2026-05-16T05:23:49Z","snapshot_observed_at":"2026-07-06T23:27:52.850836Z","submitted_at":"2026-05-16T05:23:49Z","title":"Observation-Aligned Mask Priors for Learning Physical Dynamics from Authentic Occlusions","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-19T20:59:55.644530Z"},"links":{"cited_paper":"/paper/2308.05732","citing_paper":"/paper/2605.16818"},"observation_digest":"sha256:46fc620b545011dca7ed22a9ef72a9aaefc0c19f397c20c609709a8103234e22","observation_id":"7f179b1f-c43d-42ac-825b-0c3e1af1f3b7","resolution":{"observed_at":"2026-05-19T21:02:47.305307Z","resolver_source":"arxiv_id","status":"verified_exact"},"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"}},{"citation":{"cited_paper":{"arxiv_id":"2308.05732","last_updated":"2023-10-21T15:41:47Z","snapshot_observed_at":"2026-07-06T16:04:58.235073Z","submitted_at":"2023-08-10T17:53:05Z","title":"PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers","version":2},"cited_work":{"arxiv_id":"2308.05732","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2308.05732","snapshot_observed_at":"2026-07-02T02:06:27.733905Z","title":"Veeling, Paris Perdikaris, Richard E","venue":null,"work_id":"2d0690ba-4a99-441e-a401-83b45a0ef7b2","year":2023},"citing_paper":{"arxiv_id":"2605.27722","last_updated":"2026-05-26T21:49:16Z","snapshot_observed_at":"2026-08-07T15:16:19.934941Z","submitted_at":"2026-05-26T21:49:16Z","title":"NUCLEUS-MoE: Unified Model of Pool Boiling for Liquid Cooling","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-29T18:30:09.813448Z"},"links":{"cited_paper":"/paper/2308.05732","citing_paper":"/paper/2605.27722"},"observation_digest":"sha256:d52d31f622f22c91337d462171e790d482cf9c9a71aa561be00e53f3e6fb68ad","observation_id":"de3dab7d-b450-490b-aa40-66a2502e5ae8","resolution":{"observed_at":"2026-06-29T18:33:50.524131Z","resolver_source":"arxiv_id","status":"verified_exact"},"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"}},{"citation":{"cited_paper":{"arxiv_id":"2308.05732","last_updated":"2023-10-21T15:41:47Z","snapshot_observed_at":"2026-07-06T16:04:58.235073Z","submitted_at":"2023-08-10T17:53:05Z","title":"PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers","version":2},"cited_work":{"arxiv_id":"2308.05732","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2308.05732","snapshot_observed_at":"2026-07-02T02:06:27.733905Z","title":"Veeling, Paris Perdikaris, Richard E","venue":null,"work_id":"2d0690ba-4a99-441e-a401-83b45a0ef7b2","year":2023},"citing_paper":{"arxiv_id":"2605.28851","last_updated":"2026-05-16T20:37:05Z","snapshot_observed_at":"2026-07-06T23:38:23.512460Z","submitted_at":"2026-05-16T20:37:05Z","title":"Towards a Foundation Model for the Martian Atmosphere","version":1},"reference_index":175,"source":"pdf_text","source_observed_at":"2026-06-30T19:01:31.373340Z"},"links":{"cited_paper":"/paper/2308.05732","citing_paper":"/paper/2605.28851"},"observation_digest":"sha256:b81bb330ca07565775a54756105a57ee2f6221d3e77778c9b9a6a19fb7aa58f2","observation_id":"8244c6cb-fe16-4716-b44e-9189b57d8586","resolution":{"observed_at":"2026-06-30T19:05:00.830767Z","resolver_source":"arxiv_id","status":"verified_exact"},"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"}},{"citation":{"cited_paper":{"arxiv_id":"2308.05732","last_updated":"2023-10-21T15:41:47Z","snapshot_observed_at":"2026-07-06T16:04:58.235073Z","submitted_at":"2023-08-10T17:53:05Z","title":"PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers","version":2},"cited_work":{"arxiv_id":"2308.05732","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2308.05732","snapshot_observed_at":"2026-07-02T02:06:27.733905Z","title":"Veeling, Paris Perdikaris, Richard E","venue":null,"work_id":"2d0690ba-4a99-441e-a401-83b45a0ef7b2","year":2023},"citing_paper":{"arxiv_id":"2606.03936","last_updated":"2026-06-02T17:26:15Z","snapshot_observed_at":"2026-08-08T03:17:34.020268Z","submitted_at":"2026-06-02T17:26:15Z","title":"Correcting Neural Operator Spectral Bias via Diffusion Posterior Sampling with Sparse Observations","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-28T11:10:52.822919Z"},"links":{"cited_paper":"/paper/2308.05732","citing_paper":"/paper/2606.03936"},"observation_digest":"sha256:2e5bc1bfb44dc7d22177b9e967010c66c57e799b5bce3b7dcdeb52b2e0371f2f","observation_id":"e4668083-b7f6-414e-8e52-4c903af7c5cd","resolution":{"observed_at":"2026-07-02T02:06:27.736205Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2308.05732","last_updated":"2023-10-21T15:41:47Z","snapshot_observed_at":"2026-07-06T16:04:58.235073Z","submitted_at":"2023-08-10T17:53:05Z","title":"PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.05732","snapshot_observed_at":"2026-07-14T12:34:40.550770Z","title":"Huakun Luo, Haixu Wu, Hang Zhou, Lanxiang Xing, Yichen Di, Jianmin Wang, and Mingsheng Long","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.10333","last_updated":"2026-07-11T14:19:51Z","snapshot_observed_at":"2026-07-16T23:18:18.844503Z","submitted_at":"2026-07-11T14:19:51Z","title":"NeuroForge: A Self-Correcting, Geometry-Native Neural CFD Engine with Calibrated Physics-Residual Trust","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-14T12:34:40.550770Z"},"links":{"cited_paper":"/paper/2308.05732","citing_paper":"/paper/2607.10333"},"observation_digest":"sha256:8648f7b93edb351d838f82b3426b553a5257f5569bf1693fa93fafe0e8de9b72","observation_id":"390921f9-bae7-479a-977e-8cccd9abde8d","resolution":{"observed_at":"2026-07-14T12:34:40.550770Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.05732","last_updated":"2023-10-21T15:41:47Z","snapshot_observed_at":"2026-07-06T16:04:58.235073Z","submitted_at":"2023-08-10T17:53:05Z","title":"PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.05732","snapshot_observed_at":"2026-08-01T10:13:41.463768Z","title":"S., Perdikaris, P., Turner, R","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20321","last_updated":"2026-07-23T06:22:08Z","snapshot_observed_at":"2026-08-07T18:16:59.476229Z","submitted_at":"2026-07-22T16:07:09Z","title":"Label-Free Finite-Volume-Residual Training of Attention Graph Neural Networks for Coupled Thermo-Fluid Fields","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-01T10:13:41.463768Z"},"links":{"cited_paper":"/paper/2308.05732","citing_paper":"/paper/2607.20321"},"observation_digest":"sha256:9ce1b06abf7434977f1b4c8463afc894491492b61a84a733a8f1e8195a22665c","observation_id":"33c7cdce-96cb-43ba-b960-fd7e1dfeca01","resolution":{"observed_at":"2026-08-01T10:13:41.463768Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.05732","last_updated":"2023-10-21T15:41:47Z","snapshot_observed_at":"2026-07-06T16:04:58.235073Z","submitted_at":"2023-08-10T17:53:05Z","title":"PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.05732","snapshot_observed_at":"2026-07-30T16:23:26.571003Z","title":"Lippe, B","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.23667","last_updated":"2026-07-26T14:02:46Z","snapshot_observed_at":"2026-08-07T14:03:08.676589Z","submitted_at":"2026-07-26T14:02:46Z","title":"No Free Lunch in Flow Surrogates under Time-Varying Boundary Conditions: A Two-Regime Study","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-30T16:23:26.571003Z"},"links":{"cited_paper":"/paper/2308.05732","citing_paper":"/paper/2607.23667"},"observation_digest":"sha256:4fac4aed506dbea2662742fd6545f8d61816d2a24cf83fd56647eeef28097c7b","observation_id":"6919013c-1a62-4826-86d1-7f3357592f43","resolution":{"observed_at":"2026-07-30T16:23:26.571003Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2308.05732/citation-record","integrity":"/paper/2308.05732/integrity","json":"/paper/2308.05732/citation-record.json","paper":"/paper/2308.05732"},"outbound":[],"paper":{"arxiv_id":"2308.05732","last_updated":"2023-10-21T15:41:47Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T16:04:58.235073Z","submitted_at":"2023-08-10T17:53:05Z","title":"PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers"},"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 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2308.05732."}