{"as_of":"2026-08-10T15:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:53974ecb6c47e0c3ae7b4a0623e61a252edb90799851434835009d2316115f36","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":13,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":13,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":13,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":13,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T21:49:40.243310Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"doi_reference","source_observed_at":"2026-07-09T12:36:13.696486Z","state":"measured"}],"external_citation_measurements":[{"count":2470,"observed_at":"2026-07-09T12:36:13.696486Z","source":"doi_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1905.11075","last_updated":"2020-01-04T11:27:06Z","snapshot_observed_at":"2026-08-06T10:12:47.028023Z","submitted_at":"2019-05-27T09:26:17Z","title":"Machine Learning for Fluid Mechanics","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.11075","snapshot_observed_at":"2026-08-07T21:49:40.243310Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.09346","last_updated":"2025-02-13T14:11:33Z","snapshot_observed_at":"2026-08-09T01:24:29.114735Z","submitted_at":"2025-02-13T14:11:33Z","title":"Machine learning for modelling unstructured grid data in computational physics: a review","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T21:49:40.243310Z"},"links":{"cited_paper":"/paper/1905.11075","citing_paper":"/paper/2502.09346"},"observation_digest":"sha256:129bfa4a3389c9f4727ec29401ff509f0c98895b2ec15a49b203b4a9fd0fadb1","observation_id":"52584425-b6e9-4017-abef-14d0ae08d9b3","resolution":{"observed_at":"2026-08-07T21:49:40.243310Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.11075","last_updated":"2020-01-04T11:27:06Z","snapshot_observed_at":"2026-08-06T10:12:47.028023Z","submitted_at":"2019-05-27T09:26:17Z","title":"Machine Learning for Fluid Mechanics","version":3},"cited_work":{"arxiv_id":"1905.11075","doi":"10.1146/annurev-fluid-010719-060214","metadata_source":"doi_reference","pith_arxiv_id":"1905.11075","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Brunton, Bernd R","venue":"Annual Review of Fluid Mechanics","work_id":"4a3f499f-284f-4398-8530-16f2925c0347","year":2020},"citing_paper":{"arxiv_id":"2507.21437","last_updated":"2026-04-03T06:54:21Z","snapshot_observed_at":"2026-08-04T02:24:14.702296Z","submitted_at":"2025-07-29T02:16:14Z","title":"PVD-ONet: A Multi-scale Neural Operator Method for Singularly Perturbed Boundary Layer Problems","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-19T03:09:22.055541Z"},"links":{"cited_paper":"/paper/1905.11075","citing_paper":"/paper/2507.21437"},"observation_digest":"sha256:5dfbf320789b37832aadeeee0f94abcf7ca29d79f2249fb6b53965c33ea493a2","observation_id":"1996591b-3786-4972-9364-cb2a395951f2","resolution":{"observed_at":"2026-05-19T03:12:00.446390Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-05-20T19:52:42.763794+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-20T19:52:42.763794+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.11075","last_updated":"2020-01-04T11:27:06Z","snapshot_observed_at":"2026-08-06T10:12:47.028023Z","submitted_at":"2019-05-27T09:26:17Z","title":"Machine Learning for Fluid Mechanics","version":3},"cited_work":{"arxiv_id":"1905.11075","doi":"10.1146/annurev-fluid-010719-060214","metadata_source":"doi_reference","pith_arxiv_id":"1905.11075","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Brunton, Bernd R","venue":"Annual Review of Fluid Mechanics","work_id":"4a3f499f-284f-4398-8530-16f2925c0347","year":2020},"citing_paper":{"arxiv_id":"2604.18953","last_updated":"2026-04-24T17:45:10Z","snapshot_observed_at":"2026-07-06T23:05:44.499005Z","submitted_at":"2026-04-21T01:04:26Z","title":"FlowForge: A Staged Local Rollout Engine for Flow-Field Prediction","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-10T03:14:36.464122Z"},"links":{"cited_paper":"/paper/1905.11075","citing_paper":"/paper/2604.18953"},"observation_digest":"sha256:06b384d693c6e6baec3e38ae39edc4d21355e8e5ed5519ff23a4ae03c45cfdfa","observation_id":"4b8ea918-5488-4cc9-97f9-95635c569b42","resolution":{"observed_at":"2026-05-10T03:24:14.693679Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-05-20T19:52:42.763794+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-20T19:52:42.763794+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.11075","last_updated":"2020-01-04T11:27:06Z","snapshot_observed_at":"2026-08-06T10:12:47.028023Z","submitted_at":"2019-05-27T09:26:17Z","title":"Machine Learning for Fluid Mechanics","version":3},"cited_work":{"arxiv_id":"1905.11075","doi":"10.1146/annurev-fluid-010719-060214","metadata_source":"doi_reference","pith_arxiv_id":"1905.11075","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Brunton, Bernd R","venue":"Annual Review of Fluid Mechanics","work_id":"4a3f499f-284f-4398-8530-16f2925c0347","year":2020},"citing_paper":{"arxiv_id":"2604.25617","last_updated":"2026-04-28T13:24:25Z","snapshot_observed_at":"2026-08-04T01:45:28.496918Z","submitted_at":"2026-04-28T13:24:25Z","title":"AI-Powered Surrogate Modelling for Multiscale Combustion: A Critical Review and Opportunities","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-07T14:13:10.563497Z"},"links":{"cited_paper":"/paper/1905.11075","citing_paper":"/paper/2604.25617"},"observation_digest":"sha256:90396b35f9d4f4cdff655d564a17544a82bf799e023b97ff2ec91c97a182d9b1","observation_id":"7897b3e2-e12a-40a2-bb87-d39ac7f7956e","resolution":{"observed_at":"2026-05-09T02:59:51.213183Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-05-20T19:52:42.763794+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-20T19:52:42.763794+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.11075","last_updated":"2020-01-04T11:27:06Z","snapshot_observed_at":"2026-08-06T10:12:47.028023Z","submitted_at":"2019-05-27T09:26:17Z","title":"Machine Learning for Fluid Mechanics","version":3},"cited_work":{"arxiv_id":"1905.11075","doi":"10.1146/annurev-fluid-010719-060214","metadata_source":"doi_reference","pith_arxiv_id":"1905.11075","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Brunton, Bernd R","venue":"Annual Review of Fluid Mechanics","work_id":"4a3f499f-284f-4398-8530-16f2925c0347","year":2020},"citing_paper":{"arxiv_id":"2604.25824","last_updated":"2026-04-28T16:32:18Z","snapshot_observed_at":"2026-07-06T23:11:39.318906Z","submitted_at":"2026-04-28T16:32:18Z","title":"Discovery of Sparse Invariant Subgrid-Scale Closures via Dissipation-Controlled Training for Large Eddy Simulation on Anisotropic Grids","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-07T14:58:37.005685Z"},"links":{"cited_paper":"/paper/1905.11075","citing_paper":"/paper/2604.25824"},"observation_digest":"sha256:090f7285e2f978ca0e59c1bdac93d5c0b86e5d3a8a21d0287bdc4930c15fe791","observation_id":"d1b4cd5c-019b-4504-85d4-6b930ddbcc20","resolution":{"observed_at":"2026-05-09T02:39:51.392736Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-05-20T19:52:42.763794+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-20T19:52:42.763794+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.11075","last_updated":"2020-01-04T11:27:06Z","snapshot_observed_at":"2026-08-06T10:12:47.028023Z","submitted_at":"2019-05-27T09:26:17Z","title":"Machine Learning for Fluid Mechanics","version":3},"cited_work":{"arxiv_id":"1905.11075","doi":"10.1146/annurev-fluid-010719-060214","metadata_source":"doi_reference","pith_arxiv_id":"1905.11075","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Brunton, Bernd R","venue":"Annual Review of Fluid Mechanics","work_id":"4a3f499f-284f-4398-8530-16f2925c0347","year":2020},"citing_paper":{"arxiv_id":"2604.26621","last_updated":"2026-04-29T12:46:00Z","snapshot_observed_at":"2026-08-07T00:19:55.648763Z","submitted_at":"2026-04-29T12:46:00Z","title":"Large-eddy simulation nets (LESnets) based on physics-informed neural operator for wall-bounded turbulence","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-07T10:58:07.460227Z"},"links":{"cited_paper":"/paper/1905.11075","citing_paper":"/paper/2604.26621"},"observation_digest":"sha256:6a57e951aa5eb2e25d7ff1aba7c8dc58bdce28661ecdd7ccaba74b1dd5848472","observation_id":"801867bc-617d-4439-b61b-5cdb1f9b5547","resolution":{"observed_at":"2026-05-09T04:00:13.072556Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-05-20T19:52:42.763794+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-20T19:52:42.763794+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.11075","last_updated":"2020-01-04T11:27:06Z","snapshot_observed_at":"2026-08-06T10:12:47.028023Z","submitted_at":"2019-05-27T09:26:17Z","title":"Machine Learning for Fluid Mechanics","version":3},"cited_work":{"arxiv_id":"1905.11075","doi":"10.1146/annurev-fluid-010719-060214","metadata_source":"doi_reference","pith_arxiv_id":"1905.11075","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Brunton, Bernd R","venue":"Annual Review of Fluid Mechanics","work_id":"4a3f499f-284f-4398-8530-16f2925c0347","year":2020},"citing_paper":{"arxiv_id":"2605.00510","last_updated":"2026-05-01T08:36:52Z","snapshot_observed_at":"2026-07-06T23:13:57.367556Z","submitted_at":"2026-05-01T08:36:52Z","title":"Scale-Aware Adversarial Analysis: A Diagnostic for Generative AI in Multiscale Complex Systems","version":1},"reference_index":91,"source":"arxiv_source","source_observed_at":"2026-05-09T19:56:13.911743Z"},"links":{"cited_paper":"/paper/1905.11075","citing_paper":"/paper/2605.00510"},"observation_digest":"sha256:b9390d1b3979b15bf6fc19fcd5d8b78864a1646ee40d8414edb67959b6e9fab2","observation_id":"67742505-9c1a-420e-b56d-15b6b4f00bab","resolution":{"observed_at":"2026-05-09T19:56:16.488013Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-05-20T19:52:42.763794+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-20T19:52:42.763794+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.11075","last_updated":"2020-01-04T11:27:06Z","snapshot_observed_at":"2026-08-06T10:12:47.028023Z","submitted_at":"2019-05-27T09:26:17Z","title":"Machine Learning for Fluid Mechanics","version":3},"cited_work":{"arxiv_id":"1905.11075","doi":"10.1146/annurev-fluid-010719-060214","metadata_source":"doi_reference","pith_arxiv_id":"1905.11075","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Brunton, Bernd R","venue":"Annual Review of Fluid Mechanics","work_id":"4a3f499f-284f-4398-8530-16f2925c0347","year":2020},"citing_paper":{"arxiv_id":"2605.19076","last_updated":"2026-05-18T20:05:52Z","snapshot_observed_at":"2026-08-03T04:44:14.538042Z","submitted_at":"2026-05-18T20:05:52Z","title":"The impact of observation density on Bayesian inversion of latent dynamics in shock-dominated flows","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-20T11:38:18.834765Z"},"links":{"cited_paper":"/paper/1905.11075","citing_paper":"/paper/2605.19076"},"observation_digest":"sha256:78d99b0ce7a5183c6ea8325ca41d49f4eac83eb9548bc1d3d7b7691ae4632068","observation_id":"ae79db1c-24c6-4bad-a2c4-081a1ad60a8f","resolution":{"observed_at":"2026-05-20T11:43:14.850686Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-05-20T19:52:42.763794+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-20T19:52:42.763794+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.11075","last_updated":"2020-01-04T11:27:06Z","snapshot_observed_at":"2026-08-06T10:12:47.028023Z","submitted_at":"2019-05-27T09:26:17Z","title":"Machine Learning for Fluid Mechanics","version":3},"cited_work":{"arxiv_id":"1905.11075","doi":"10.1146/annurev-fluid-010719-060214","metadata_source":"doi_reference","pith_arxiv_id":"1905.11075","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Brunton, Bernd R","venue":"Annual Review of Fluid Mechanics","work_id":"4a3f499f-284f-4398-8530-16f2925c0347","year":2020},"citing_paper":{"arxiv_id":"2605.26324","last_updated":"2026-05-25T21:00:29Z","snapshot_observed_at":"2026-08-04T17:08:25.657601Z","submitted_at":"2026-05-25T21:00:29Z","title":"Semigroup Consistency as a Diagnostic for Learned Physics Simulators","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-29T22:19:32.034640Z"},"links":{"cited_paper":"/paper/1905.11075","citing_paper":"/paper/2605.26324"},"observation_digest":"sha256:d2f87182b8574ad997352563478298748526b804a79bfd38d6838bb4f33596d7","observation_id":"018f3777-0d24-4795-9dd5-d84b27e5a4ed","resolution":{"observed_at":"2026-06-29T22:23:59.633357Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-05-20T19:52:42.763794+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-20T19:52:42.763794+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.11075","last_updated":"2020-01-04T11:27:06Z","snapshot_observed_at":"2026-08-06T10:12:47.028023Z","submitted_at":"2019-05-27T09:26:17Z","title":"Machine Learning for Fluid Mechanics","version":3},"cited_work":{"arxiv_id":"1905.11075","doi":"10.1146/annurev-fluid-010719-060214","metadata_source":"doi_reference","pith_arxiv_id":"1905.11075","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Brunton, Bernd R","venue":"Annual Review of Fluid Mechanics","work_id":"4a3f499f-284f-4398-8530-16f2925c0347","year":2020},"citing_paper":{"arxiv_id":"2605.30112","last_updated":"2026-05-28T15:49:26Z","snapshot_observed_at":"2026-08-05T05:31:09.132940Z","submitted_at":"2026-05-28T15:49:26Z","title":"Striding Across Reynolds Numbers: Representation Geometry in Neural PDE Generalisation","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-06-29T08:53:04.122132Z"},"links":{"cited_paper":"/paper/1905.11075","citing_paper":"/paper/2605.30112"},"observation_digest":"sha256:4838126fa4ad0b030c80c72ea73b96f002365f5cdf05d34ae42012825bf9124f","observation_id":"2d2a9482-190d-4859-9932-73028500b025","resolution":{"observed_at":"2026-06-29T08:53:15.319819Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-05-20T19:52:42.763794+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-20T19:52:42.763794+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.11075","last_updated":"2020-01-04T11:27:06Z","snapshot_observed_at":"2026-08-06T10:12:47.028023Z","submitted_at":"2019-05-27T09:26:17Z","title":"Machine Learning for Fluid Mechanics","version":3},"cited_work":{"arxiv_id":"1905.11075","doi":"10.1146/annurev-fluid-010719-060214","metadata_source":"doi_reference","pith_arxiv_id":"1905.11075","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Brunton, Bernd R","venue":"Annual Review of Fluid Mechanics","work_id":"4a3f499f-284f-4398-8530-16f2925c0347","year":2020},"citing_paper":{"arxiv_id":"2606.13336","last_updated":"2026-06-11T13:27:22Z","snapshot_observed_at":"2026-08-06T00:18:54.613093Z","submitted_at":"2026-06-11T13:27:22Z","title":"Data-Driven Equation Discovery for Nonlinear Liquid Film Flows","version":1},"reference_index":108,"source":"arxiv_source","source_observed_at":"2026-06-27T05:50:38.293060Z"},"links":{"cited_paper":"/paper/1905.11075","citing_paper":"/paper/2606.13336"},"observation_digest":"sha256:a9bc4fbdf575c13b41a49b029981da0f970b1ad73ec1b0beded6266d2e044da9","observation_id":"628909a9-f39d-4cd4-afe4-9db9f161f096","resolution":{"observed_at":"2026-06-27T06:00:36.575252Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-05-20T19:52:42.763794+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-20T19:52:42.763794+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.11075","last_updated":"2020-01-04T11:27:06Z","snapshot_observed_at":"2026-08-06T10:12:47.028023Z","submitted_at":"2019-05-27T09:26:17Z","title":"Machine Learning for Fluid Mechanics","version":3},"cited_work":{"arxiv_id":"1905.11075","doi":"10.1146/annurev-fluid-010719-060214","metadata_source":"doi_reference","pith_arxiv_id":"1905.11075","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Brunton, Bernd R","venue":"Annual Review of Fluid Mechanics","work_id":"4a3f499f-284f-4398-8530-16f2925c0347","year":2020},"citing_paper":{"arxiv_id":"2607.07385","last_updated":"2026-07-08T13:17:00Z","snapshot_observed_at":"2026-08-07T12:01:04.422859Z","submitted_at":"2026-07-08T13:17:00Z","title":"JAX-FVM: A differentiable, entropy-stable finite volume solver on unstructured meshes for compressible flows","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-09T12:31:43.303029Z"},"links":{"cited_paper":"/paper/1905.11075","citing_paper":"/paper/2607.07385"},"observation_digest":"sha256:afe407ef397e04c6298631f32d86f1bb216556161f593d8449d9e940a390418f","observation_id":"2b0d449e-f4c4-451d-b2ac-2921ebaa2647","resolution":{"observed_at":"2026-07-09T12:36:13.699456Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-05-20T19:52:42.763794+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-20T19:52:42.763794+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.11075","last_updated":"2020-01-04T11:27:06Z","snapshot_observed_at":"2026-08-06T10:12:47.028023Z","submitted_at":"2019-05-27T09:26:17Z","title":"Machine Learning for Fluid Mechanics","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.11075","snapshot_observed_at":"2026-08-01T08:23:28.505957Z","title":"arXiv preprint arXiv:1905.11075 , year=","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2607.21152","last_updated":"2026-07-23T10:36:05Z","snapshot_observed_at":"2026-08-09T22:50:00.688633Z","submitted_at":"2026-07-23T10:36:05Z","title":"A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-01T08:23:28.505957Z"},"links":{"cited_paper":"/paper/1905.11075","citing_paper":"/paper/2607.21152"},"observation_digest":"sha256:51bb8a0e44cff95d52a2d9336770d5423d80bf874e2fc25715b57a208ca28d6f","observation_id":"86a5a3cd-8440-43d6-b20c-2f710f531a93","resolution":{"observed_at":"2026-08-01T08:23:28.505957Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1905.11075/citation-record","integrity":"/paper/1905.11075/integrity","json":"/paper/1905.11075/citation-record.json","paper":"/paper/1905.11075"},"outbound":[],"paper":{"arxiv_id":"1905.11075","last_updated":"2020-01-04T11:27:06Z","latest_version":3,"primary_category":"physics.flu-dyn","snapshot_observed_at":"2026-08-06T10:12:47.028023Z","submitted_at":"2019-05-27T09:26:17Z","title":"Machine Learning for Fluid Mechanics"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:1905.11075."}