{"as_of":"2026-08-08T02:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:949c3ff651620c9d2c43a867797d5cb23874f86107cf8a3980cfc6c46da9681e","coverage":[{"denominator":91,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":91,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T16:18:52.852949Z","state":"measured"},{"denominator":91,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":91,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2608.03600/citation-record","integrity":"/paper/2608.03600/integrity","json":"/paper/2608.03600/citation-record.json","paper":"/paper/2608.03600"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:44.080674Z","title":"Evans.Partial Differential Equations, volume 19 ofGraduate Studies in Mathe- matics","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:44.080674Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:0f63a5103df2c5998241ab308af32d8266816b6f151a93299e700f1524e66919","observation_id":"34a98559-151c-4dfa-8f66-13ccb354ab7f","resolution":{"observed_at":"2026-08-05T16:18:44.080674Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:44.142369Z","title":null,"venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:44.142369Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:d5a6a45eee4c0b1de713f6f86f80187b858f12bc2f85414d7c198fd7f06ccb2c","observation_id":"da36b192-5b2a-430c-acc7-208ece00472a","resolution":{"observed_at":"2026-08-05T16:18:44.142369Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:44.226996Z","title":"Oberkampf and Timothy G","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:44.226996Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:a056367203ffd07a62f46293ba614bcf043aa619dec32dabac9e31e5ba4c3dbd","observation_id":"d4b8fba1-23ac-40d0-b005-26015b0828c9","resolution":{"observed_at":"2026-08-05T16:18:44.226996Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:44.332723Z","title":"Prentice Hall, Upper Saddle River, NJ, 2 edition, 1999","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:44.332723Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:e443da0a41dbf80945995fe9bed86b6beda2eedfd67b66e989c9f73da2b21c45","observation_id":"ca0ce083-75cd-465d-85df-d51f761efb6a","resolution":{"observed_at":"2026-08-05T16:18:44.332723Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:44.461131Z","title":"LeVeque.Finite Difference Methods for Ordinary and Partial Differential Equations: Steady-State and Time-Dependent Problems","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:44.461131Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:dead8f7cb25acec8cd6dddbe1a17cd79aaa3631a36ab400eeada0fd7b7ebf4ab","observation_id":"e809e583-7bb2-4a2f-896a-029e527fa3f7","resolution":{"observed_at":"2026-08-05T16:18:44.461131Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:44.532390Z","title":null,"venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:44.532390Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:742699489d685c140895a9d760f42e82038e239bca0983dac73d539ce56bc83b","observation_id":"834c796f-f8da-4860-a8ef-3da92ab4c0f4","resolution":{"observed_at":"2026-08-05T16:18:44.532390Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:44.617867Z","title":"Brenner and L","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:44.617867Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:27c6d8a47bcdb6df83cd6e195b3f43f250dacceb6e5d5068bc59885d8a28bcf8","observation_id":"671728f8-7ad9-4916-89f9-0ba80d85b12e","resolution":{"observed_at":"2026-08-05T16:18:44.617867Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:44.718210Z","title":"Trefethen.Spectral Methods in MATLAB","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:44.718210Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:27b727ac98527a9e0becfe7d3ce185489ca1262382826947ac568a5c50a5907b","observation_id":"742e39ea-51be-479e-9bc8-6e4871cca775","resolution":{"observed_at":"2026-08-05T16:18:44.718210Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:44.778448Z","title":"Review of discontinuous galerkin finite element methods for partial differential equations on complicated domains","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:44.778448Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:8f1e66a1759e7c86f289e2e5712b2a006170f9957056f20fb677ecf66a1dd321","observation_id":"32e1e83c-e8b0-456c-941f-03703802e7be","resolution":{"observed_at":"2026-08-05T16:18:44.778448Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:44.855748Z","title":"A review of mesh adaptation technology applied to computational fluid dynamics.Fluids, 10(5):129, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:44.855748Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:15c6b8cf8de40fc207c3b73cb1b04ac9be7febb2e96392b7c435a1fef16b56ab","observation_id":"fa4abf32-a90f-41cd-b45d-3eb704bc37de","resolution":{"observed_at":"2026-08-05T16:18:44.855748Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:44.976610Z","title":"Springer, Berlin, Heidelberg, 1971","venue":null,"work_id":null,"year":1971},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:44.976610Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:25f656173e257b7f8469c37a1a8fdfa4f4211eb55f93d6d8285e174b3e9dc188","observation_id":"09346225-dfea-4b2c-a292-6afb35bd6268","resolution":{"observed_at":"2026-08-05T16:18:44.976610Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:06.126154Z","title":"Springer, Dor- drecht, 2009","venue":null,"work_id":"95b48cb1-7b25-4b1f-a775-c4cfc00c0cca","year":2009},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:45.107034Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:82e8e5a6e89a5818271e261f9f20a9bef1ab7911d9ba6eb7f8d982c49531748b","observation_id":"83ce9f33-270a-4ba3-8b11-30f10f964e76","resolution":{"observed_at":"2026-08-05T16:19:06.192318Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:05.883147Z","title":"Gunzburger.Perspectives in Flow Control and Optimization","venue":null,"work_id":"a0980846-ba0d-44f5-b78f-0d5aca8915b3","year":2002},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:45.226793Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:dc0db4a33e88b1d8951ea812c64015bfa04c8aa3186f54b5a7b6ab9f2907f2a9","observation_id":"7989a46f-6778-4773-962a-0856228739a7","resolution":{"observed_at":"2026-08-05T16:19:05.984497Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:05.713422Z","title":"Bendsøe and Ole Sigmund.Topology Optimization: Theory, Methods, and Applica- tions","venue":null,"work_id":"52782e3b-3c31-49c3-9374-4c33b8bd8cb7","year":2004},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:45.318804Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:6a38734f8092cbad809be711eb748ffa28449387e87079043f0371e0b534861c","observation_id":"d5e1172a-f6f5-4ddc-8355-6bd9a8d75ebf","resolution":{"observed_at":"2026-08-05T16:19:05.786848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:45.462230Z","title":"Brunton, Joshua L","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:45.462230Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:997ff5f35c8203e1767b5cdfbafdd500dfa7b33d9e7a75b56577bb98474f51f9","observation_id":"f93466f6-5816-4ec0-a0a4-7c6beb0c5380","resolution":{"observed_at":"2026-08-05T16:18:45.462230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:45.590133Z","title":"Rudy, Steven L","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:45.590133Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:b4493e5f7bbfa809067afd9523e0ef16e15712b3d0129cc915e6ab30dcfa9ead","observation_id":"06e7c1a8-9326-4867-8e19-0dfd58e94171","resolution":{"observed_at":"2026-08-05T16:18:45.590133Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:05.499187Z","title":"Data-driven equation discovery of ocean mesoscale closures","venue":null,"work_id":"814b56f4-0e1f-42ec-bc4c-8674107d1995","year":2020},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:45.731443Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:ef12559f781f421cfdc49e24df2bbdbb9027013e243d24946dbb4ae2d60c6361","observation_id":"705900b0-b8d9-4ca3-9da9-02a1df26dc09","resolution":{"observed_at":"2026-08-05T16:19:05.603649Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:05.262760Z","title":"Formulating turbulence closures using sparse regression with embedded form invariance.Physical Review Fluids, 5(8):084611, 2020","venue":null,"work_id":"7f57b1f5-b33f-4561-9c20-bcec8c6b0b77","year":2020},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:45.845010Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:4c28f2af3863ea04c1b5d370cfc45c912fc44cb52d97d3b7f2f570b394633758","observation_id":"a140e736-fafe-4954-9018-0a7e2f3e00b6","resolution":{"observed_at":"2026-08-05T16:19:05.387241Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:05.066684Z","title":"Data-driven discovery of coarse-grained equations","venue":null,"work_id":"9b70176a-e2a9-4269-9c2a-cfac0b6257bf","year":2021},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:45.989309Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:9bfa530a76f84eabd7d840a61d4b69e8c02d8b399962a1d18ab7654f81ea1a7c","observation_id":"343f0245-c043-402a-bd6a-84a838c4afb0","resolution":{"observed_at":"2026-08-05T16:19:05.158617Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:46.157043Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:46.157043Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:866ed612e9b1a40b73a8564f9cc24f58a8c9e28d302f2d37b56a9db5f9a1b972","observation_id":"a6de3e7e-c341-4761-8193-affbbb38e549","resolution":{"observed_at":"2026-08-05T16:18:46.157043Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:04.808485Z","title":null,"venue":null,"work_id":"c8fb5e34-6a99-4aa1-9788-309fcec2fc95","year":2019},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:46.313488Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:296631111418e1635f0c3758d8593a68dfac45c03f0fea59a9447a9aa4188c28","observation_id":"b04bf933-a70b-4774-81b9-fbce346b5242","resolution":{"observed_at":"2026-08-05T16:19:04.934620Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:04.609984Z","title":"Smith, Ayya Alieva, Qing Wang, Michael P","venue":null,"work_id":"ef27cf60-0c0b-4317-99cb-cba7e3fde20c","year":2021},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:46.431680Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:f95c1622ae3cd71c78db37a335f51d505606b0588d9de214e6d5b22ab240546d","observation_id":"5d631dab-8dab-42a3-a68b-e7ddf57c1d6b","resolution":{"observed_at":"2026-08-05T16:19:04.713134Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:46.540551Z","title":"Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:46.540551Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:b3d3c1b6eb0e0c6a7a6fbf549ef2af34e8f74747d9b708f3d06420a1f779ab4e","observation_id":"c6d342bd-e9d8-4d92-ad2a-b8256d8d8efe","resolution":{"observed_at":"2026-08-05T16:18:46.540551Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:04.496128Z","title":"Fourier neural operator for parametric partial differential equations","venue":null,"work_id":"28c1e43e-a985-45e5-a7dd-081340e964b6","year":2021},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:46.656647Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:9fa789cd4dbb59a8dbc6163f89e4e5d29a2bfd0570bc6702657e401033a21d12","observation_id":"a3b5042f-c961-4a86-b0e0-4fe440939b41","resolution":{"observed_at":"2026-08-05T16:19:04.546189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:46.708343Z","title":"Factorized Fourier neural operators","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:46.708343Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:e0fce30389665bc2f282a4d3652c6120d6e942d89f9b2f8f188045335eb1e9d6","observation_id":"edb3df40-3dcb-4de5-a1e2-c0a3a313be58","resolution":{"observed_at":"2026-08-05T16:18:46.708343Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:46.741318Z","title":"Physics-informed neural networks with hard constraints for inverse design.SIAM Journal on Scientific Computing, 43(6):B1105–B1132, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:46.741318Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:24379d3b88ee585190cf622ac0386ba8be8c1f77ece0bf15edb1111a7a3affb2","observation_id":"f933571a-056d-4c31-9fda-3b61c0ed073b","resolution":{"observed_at":"2026-08-05T16:18:46.741318Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:04.279383Z","title":"Gradient-enhanced physics- informed neural networks for forward and inverse pde problems.Computer Methods in Applied Mechanics and Engineering, 393:114823, 2022","venue":null,"work_id":"63ab0608-cde2-4809-a6d3-623b62ad4f25","year":2022},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:46.858792Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:ddba65768357b498c554833aae15e782a984b5dfa6077b70055e6aaf1846b719","observation_id":"d042fb22-248d-4f37-8f80-4a2351466b3b","resolution":{"observed_at":"2026-08-05T16:19:04.383908Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:04.080925Z","title":"Encoding physics to learn reaction–diffusion processes.Nature Machine Intelligence, 5(7):765–779, 2023","venue":null,"work_id":"0bfc2e94-527a-4612-8cd0-046f4416b56c","year":2023},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:46.952603Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:562371a7e2a0d01dce995be62b9b7a473beef3467a6b82f72b74083440852bd3","observation_id":"16144691-e3de-40f3-8640-99c0f8dfdf55","resolution":{"observed_at":"2026-08-05T16:19:04.168736Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:03.817406Z","title":"Pesanet: Physics-encoded spec- tral attention network for simulating pde-governed complex systems","venue":null,"work_id":"b9ec36e4-0aaf-4020-b44d-c1120c0d3569","year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:47.062895Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:2c354e6cad365489d242d03e905b0c8ebbbc73df3ea2cf9eb216e160af5e325e","observation_id":"c2302bc5-5e3d-4e3f-a44f-9b60d2896a59","resolution":{"observed_at":"2026-08-05T16:19:03.915199Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:03.592280Z","title":"Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control.Journal of Fluid Mechanics, 865:281–302, 2019","venue":null,"work_id":"c2c33ea2-546b-4fcb-8cac-52a74ada1263","year":2019},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:47.165696Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:f0b84a2939a5a19bbd8b72a2565f438ebe9a09f643edb93bdd089038ed4d0796","observation_id":"ec3874a6-5be9-48f7-9b3b-0e97307c03a2","resolution":{"observed_at":"2026-08-05T16:19:03.710999Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:03.364364Z","title":"Learning to control pdes with differentiable physics, 2020","venue":null,"work_id":"a3ef5d81-3751-4cc3-a57e-59a440505919","year":2020},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:47.274024Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:85d4d83b168226bad9f2f66b4401814f7aeac9421d28b49b5de6ff2008dd1d1d","observation_id":"0cefd96f-fdc8-41f8-b60c-51f6e97b8fd0","resolution":{"observed_at":"2026-08-05T16:19:03.502339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:03.194947Z","title":"Direct shape optimization through deep reinforcement learning.Journal of Computational Physics, 428:110080, 2021","venue":null,"work_id":"392f1b34-ea68-4c9f-893b-5179878c20d8","year":2021},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:47.365511Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:f29e16552ef95a9781a59e9c8cfa5dda78df6b22b79ff76e58015fd27f808534","observation_id":"ee71c12d-7dd6-44be-80bd-bc56edac9bab","resolution":{"observed_at":"2026-08-05T16:19:03.270362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:03.032255Z","title":"Stachenfeld, Alvaro Sanchez-Gonzalez, Pe- ter Battaglia, Jessica B","venue":null,"work_id":"53d67a7b-5d97-47a0-855e-af9f3d81f39c","year":2022},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:47.478520Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:ce13aabf6beece365bb50e701b5dddc5eceff847728e3306808cb0ff36841157","observation_id":"5802d220-0d14-4582-8960-515c591af541","resolution":{"observed_at":"2026-08-05T16:19:03.108373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:47.649677Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:47.649677Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:a19234bf9b3917e427395a9573ac13a811707f27897832928ef118a5d231db2b","observation_id":"a2ff970f-266b-496a-956c-a35ecb0d67d4","resolution":{"observed_at":"2026-08-05T16:18:47.649677Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:47.745295Z","title":"Chi, Quoc V","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:47.745295Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:491022df5b7bfd32b69fdbc413497dbc9887882ea9abd78c25d0ea0c9737d668","observation_id":"94cb1065-c665-4d57-97fc-2cb837a19c3a","resolution":{"observed_at":"2026-08-05T16:18:47.745295Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.00445","last_updated":"2022-05-01T11:01:28Z","snapshot_observed_at":"2026-08-07T07:47:07.886786Z","submitted_at":"2022-05-01T11:01:28Z","title":"MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.00445","snapshot_observed_at":"2026-08-05T16:18:47.805897Z","title":"Mielke, Yonatan Belinkov, Barak Lenz, Omer Lieber, et al","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:47.805897Z"},"links":{"cited_paper":"/paper/2205.00445","citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:ceb83447a43582ef4144471f78e801b280f1db6e344711c379408f433c648a51","observation_id":"01643b77-3029-4f91-acf1-e739d3af753b","resolution":{"observed_at":"2026-08-05T16:18:47.805897Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.07761","last_updated":"2024-07-22T07:13:18Z","snapshot_observed_at":"2026-07-06T18:13:33.931286Z","submitted_at":"2024-05-13T14:03:49Z","title":"LLM4ED: Large Language Models for Automatic Equation Discovery","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.07761","snapshot_observed_at":"2026-08-05T16:18:47.984276Z","title":"Llm4ed: Large language models for automatic equation discovery.arXiv preprint arXiv:2405.07761, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:47.984276Z"},"links":{"cited_paper":"/paper/2405.07761","citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:fb8c1605fa73b42f5a05ae4612c581b740d7a60f82da034ef079852611207175","observation_id":"688a5ed4-f226-4236-9d76-045dcf29d938","resolution":{"observed_at":"2026-08-05T16:18:47.984276Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:02.799166Z","title":"Physpde: Rethinking pde discovery and a physical hypothesis selection benchmark","venue":null,"work_id":"b0b7867e-4284-41a3-a5d1-036bb1788129","year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:48.088447Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:5393acdab198efa1768d426a5759c66a29b40b0a29bb1c131a6a784dcee80dae","observation_id":"63ebc101-381a-42f4-86ba-4acb061ec525","resolution":{"observed_at":"2026-08-05T16:19:02.891730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:02.577230Z","title":"Codepde: An inference framework for llm-driven pde solver generation","venue":null,"work_id":"4f770251-ed00-4081-b9a8-6076d2fadcc2","year":2026},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:48.189783Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:8f138eb588e33ad08abc47903d2bd385e456c80c2533e8066e8b62b913c66d47","observation_id":"a7858813-6387-49d6-b686-8dec5806e8f1","resolution":{"observed_at":"2026-08-05T16:19:02.677029Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:02.404446Z","title":"Foam-agent: Towards automated intelligent cfd workflows","venue":null,"work_id":"dbfaf99a-c7b1-4b3f-9f9e-f10376e31dd9","year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:48.243279Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:89d391b4677778ab5512c22ab218513245141a2dab699e17d25408afd8231fa6","observation_id":"1b62f2ad-3648-45fb-9175-0c0f67af63d8","resolution":{"observed_at":"2026-08-05T16:19:02.486055Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:48.328982Z","title":"Pde-sharp: Pde solver hybrids through analysis and refinement passes.arXiv preprint arXiv:2511.00183, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:48.328982Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:aaf3242b067ba051564c26fee96d57e6b7da3b6e351023256729f5bfa6561f60","observation_id":"e706ab58-f934-4d50-9c4e-2db7181f7615","resolution":{"observed_at":"2026-08-05T16:18:48.328982Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:02.287335Z","title":"Pde-controller: Llms for autoformalization and reasoning of pdes","venue":null,"work_id":"aacb1a7c-95bc-4180-b0eb-16558a310f14","year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:48.391492Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:8eca66e4e182cfecf832b28978b59b94d99200a72d86a4a671e8409761a2cb07","observation_id":"2921c93a-d007-4e4f-93e0-f3270f651e7b","resolution":{"observed_at":"2026-08-05T16:19:02.346579Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:02.088227Z","title":"Using large language models for parametric shape op- timization.Physics of Fluids, 37(8):083601, 2025","venue":null,"work_id":"7094bd65-645a-47e9-b813-d1fe77f7c771","year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:48.437046Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:ecd48421d6a268af7f43401ebdc9e7fa0a058d726799b4bc05c3e3018b76e13a","observation_id":"f6c51466-8128-464f-8736-fd0ab89b7c3a","resolution":{"observed_at":"2026-08-05T16:19:02.195485Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:01.910554Z","title":"Accelerating scientific discovery with co-scientist.Nature, 655:487–496, 2026","venue":null,"work_id":"6c8e3d22-6df5-4964-838d-8cf2273cebe0","year":2026},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:48.552823Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:53d95b4b33b8965f747af9b77f3076ae94727c9cc8c4c743b72055c97a1bd266","observation_id":"ffb367f9-d793-4aa4-842e-8bac8fce8f6c","resolution":{"observed_at":"2026-08-05T16:19:01.994569Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:01.769599Z","title":"Ghareeb, Benjamin Chang, Ludovico Mitchener, Angela Yiu, Caralyn J","venue":null,"work_id":"78e47abf-569e-4586-9c57-c9618a9a777f","year":2026},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:48.652683Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:f7930090c4db0797af26f8da869768ed6c6710768870f1e2119a51b576d4c158","observation_id":"7968a697-c3a8-4b73-a20f-9b668e78deb1","resolution":{"observed_at":"2026-08-05T16:19:01.845941Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:01.549337Z","title":"Evaluating llms’ divergent thinking capabilities for scientific idea generation with minimal context.Nature Communications, 17(1):3625, 2026","venue":null,"work_id":"c47e273c-9302-4c5c-90cd-27b99a1e4333","year":2026},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:48.757430Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:adab01b055131261f06598763ebe0bf8064287a8c81e7f7548bd898f21d70274","observation_id":"baeedead-a9f3-4c8e-aab2-9f0d80a420f3","resolution":{"observed_at":"2026-08-05T16:19:01.664646Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:01.377207Z","title":"Llm assisted mathematical modeling: Homogeneous laplace equation in cylinder with the complete electrode model","venue":null,"work_id":"6ca16081-b71a-46fc-8391-e3ca217373c1","year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:48.807285Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:cb2345c0ecfd281935dadbcdf6d6edd0207160c40810f88a511df48eff85256e","observation_id":"5c22b411-ffe8-462e-ba35-3fc2d110ef59","resolution":{"observed_at":"2026-08-05T16:19:01.459853Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:01.181987Z","title":"Agentic symbolic search: Characterizing pdes beyond hand-crafted expressions, meshes, and neural networks, 2026","venue":null,"work_id":"bf29d0eb-e60c-49dd-8303-f685addb6368","year":2026},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:48.928058Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:90b956127976a48a53e91a08b96a945facb96441d1a7dea90df6cdf28e3db6de","observation_id":"f93f45a1-85d3-4251-939c-f817a516c307","resolution":{"observed_at":"2026-08-05T16:19:01.268100Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:00.998293Z","title":"The impact of large language models on scientific discovery: a preliminary study using gpt-4","venue":null,"work_id":"7cf83c96-d8ee-46a2-9233-caa233b62669","year":2023},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:49.055822Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:a564b56154ace8bf52d798cd1f881ad958bff0892e34ed7cd2eca9c39c122849","observation_id":"2a2c1f0f-5198-4c17-982f-66129aea1fee","resolution":{"observed_at":"2026-08-05T16:19:01.083668Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.04282","last_updated":"2025-06-04T04:52:34Z","snapshot_observed_at":"2026-08-07T10:57:18.091086Z","submitted_at":"2025-06-04T04:52:34Z","title":"DrSR: LLM based Scientific Equation Discovery with Dual Reasoning from Data and Experience","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.04282","snapshot_observed_at":"2026-08-05T16:18:49.131757Z","title":"Drsr: Llm based scientific equation discovery with dual reasoning from data and experience.arXiv preprint arXiv:2506.04282, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:49.131757Z"},"links":{"cited_paper":"/paper/2506.04282","citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:50609a96efdef60fc2821126b056e688a7053b2169c281ac25817adc454858a4","observation_id":"87620b2a-cf1a-47af-b059-edb00896f0c1","resolution":{"observed_at":"2026-08-05T16:18:49.131757Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.18400","last_updated":"2025-03-20T16:37:17Z","snapshot_observed_at":"2026-07-06T18:06:49.190172Z","submitted_at":"2024-04-29T03:30:06Z","title":"LLM-SR: Scientific Equation Discovery via Programming with Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.18400","snapshot_observed_at":"2026-08-05T16:18:49.194922Z","title":"Llm-sr: Scientific equation discovery via programming with large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:49.194922Z"},"links":{"cited_paper":"/paper/2404.18400","citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:8bfd2c9ba062dcac7cdeb4f0b9ed20adacc5975178eba71c15047a18ff0aeb2c","observation_id":"e4012e50-6705-4548-b592-a4fad9b928bb","resolution":{"observed_at":"2026-08-05T16:18:49.194922Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2503.09986","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:53.238655Z","title":"From equations to insights: Unraveling symbolic structures in pdes with llms.arXiv preprint arXiv:2503.09986, 2025","venue":null,"work_id":"aec598c2-240f-40e3-aca5-f7ee2aa8b4b5","year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:49.309539Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:8a3f1d9dfcb3c6a6d3bf2c2b3f9c48541dbd4b3a52bfdb217d0fe8a454eea28b","observation_id":"f9ff3fa7-ee2f-429c-be39-52ded5a6a942","resolution":{"observed_at":"2026-08-05T16:18:53.311247Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.09783","last_updated":"2024-05-16T03:04:10Z","snapshot_observed_at":"2026-08-02T08:56:22.759898Z","submitted_at":"2024-05-16T03:04:10Z","title":"LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.09783","snapshot_observed_at":"2026-08-05T16:18:49.350413Z","title":"Llm and simulation as bilevel optimizers: A new paradigm to advance physical scientific discovery.arXiv preprint arXiv:2405.09783, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:49.350413Z"},"links":{"cited_paper":"/paper/2405.09783","citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:adfd48bcda9d3ab223110f860529e85116b77070985137e928a93152c27eeb5c","observation_id":"8a067c7b-22d9-4e8b-ba7a-298d8bfeabd0","resolution":{"observed_at":"2026-08-05T16:18:49.350413Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:00.803286Z","title":null,"venue":null,"work_id":"ab500a2d-5713-4026-b146-1022d7451102","year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:49.428273Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:3f7e5938c1fe6c501925127ecc9080b0041f1752d86335c4bcb407ef0f70b558","observation_id":"e5313d3e-2170-455f-91d5-7c3ecfc94ce5","resolution":{"observed_at":"2026-08-05T16:19:00.893189Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:00.592398Z","title":"Pdeagent-bench: A multi-metric, multi-library benchmark for pde solver generation, 2026","venue":null,"work_id":"38a8da9c-1dbd-4625-b739-8bac77d9547d","year":2026},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:49.514429Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:a823cf1205e92ae8261ac8dce832d894ef1a268597ac3ce698a3f771fbbaeefb","observation_id":"9ca5f8e2-ed19-4342-89c5-67027f22a110","resolution":{"observed_at":"2026-08-05T16:19:00.694053Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:00.399060Z","title":"Deepseek vs","venue":null,"work_id":"985f4df5-bfd2-4c70-afa1-059adb9a7d14","year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:49.615381Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:0be3de4ec05ab9cc43b7aed40bc850d44061ef0f10d2f6194ec2d2c66236ff62","observation_id":"fd0e4df3-e0d3-49e6-b851-6b07374b4f8f","resolution":{"observed_at":"2026-08-05T16:19:00.467164Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:19:00.169599Z","title":"All-fem: Agentic large language models fine-tuned for 24 finite element methods.Computer Methods in Applied Mechanics and Engineering, 457:118985, 2026","venue":null,"work_id":"ad5a5f4e-0d1f-4792-9fe4-b9d61e935d8c","year":2026},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:49.735240Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:bc9a092048115fe68de0a2bfa102493e3d473f5e783deaf85099ad57c74ec6a6","observation_id":"e91a6190-4104-4b26-90f7-b33ca2c6cf0c","resolution":{"observed_at":"2026-08-05T16:19:00.344218Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:59.976735Z","title":"Automated code development for pde solvers using large language models","venue":null,"work_id":"ca8bbe6b-5340-4d8a-954d-260ecf0db0b2","year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:49.826034Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:6686cf55f3ad9cf97e6f72b92272675825b0637d402eb9d28e0434f822d8ff4f","observation_id":"eeabfe76-604d-4e18-ba05-171304c0bfb1","resolution":{"observed_at":"2026-08-05T16:19:00.069748Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:59.789707Z","title":"Autonumerics: An autonomous, pde-agnostic multi-agent pipeline for scientific computing, 2026","venue":null,"work_id":"c18eb960-58db-4783-95e9-e49648ba9794","year":2026},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:49.904416Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:66db9f57491d2b69352de5b07e232e2e191eff1d0c56275decdaef1d3eee7001","observation_id":"94e64bfa-fc25-4367-a9d1-cb975e87a4a9","resolution":{"observed_at":"2026-08-05T16:18:59.896749Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:59.583732Z","title":"Evaluations of large language models in computa- tional fluid dynamics: Leveraging, learning and creating knowledge.Theoretical and Applied Mechanics Letters, 15(3):100597, 2025","venue":null,"work_id":"20ab01f0-6916-4cc9-ad91-bb4b675c4520","year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:50.032651Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:08ff1ffd5db726ed24a32cfae809ac74544cd469196922b5b8a4a2f516483ef3","observation_id":"fa04a730-0e79-4b78-9e5a-3346f4f2ecdd","resolution":{"observed_at":"2026-08-05T16:18:59.670106Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:59.367664Z","title":"Cfdllmbench: A benchmark suite for evaluating large language models in computational fluid dynamics","venue":null,"work_id":"390f99d4-f4a0-4033-8395-d8c49e38cec7","year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:50.083928Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:0f77c731b13cb34da3c3ea133ee844654e90b1bec2694128eb30796127001ad8","observation_id":"03e57cfc-9a0f-4b7b-babd-96a292c242c6","resolution":{"observed_at":"2026-08-05T16:18:59.444667Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:50.138829Z","title":"Openfoamgpt: A retrieval-augmented large language model (llm) agent for openfoam-based computational fluid dynamics.Physics of Fluids, 37(3), 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:50.138829Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:36ebec98e032fc5e4b729e4883ec0e883a4bb2e276d301aa3c9be8cea7ce9af8","observation_id":"903dd180-fd50-452f-86e8-ae674cd66747","resolution":{"observed_at":"2026-08-05T16:18:50.138829Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:59.125825Z","title":"Ai cfd scientist: Toward open-ended computational fluid dynamics discovery with physics-aware ai agents, 2026","venue":null,"work_id":"f6c902de-b3ab-4167-9486-5fee1e93e3df","year":2026},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:50.267832Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:2c9c27ba8db7e06e40f3d970ae8b19e27ebf1557cda86b516f050a716fb68bd4","observation_id":"69fe66e0-0f21-45c7-b2cf-b391c7fa3dc4","resolution":{"observed_at":"2026-08-05T16:18:59.269734Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:58.937486Z","title":"Openfoamgpt 2.0: End-to-end, trustworthy automation for computational fluid dynamics.International Journal of Heat and Fluid Flow, 120:110399, 2026","venue":null,"work_id":"470cf8c8-2341-41c3-937b-d9b81e48b396","year":2026},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:50.352574Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:98c155beeafead7364e9cbed6044ab1ff9b49fd5173c2e4cdfbbc3f278d39a25","observation_id":"1cf628ea-6f62-482a-9cdd-decf5c627b93","resolution":{"observed_at":"2026-08-05T16:18:59.040847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:58.674589Z","title":"Metaopenfoam: an llm-based multi-agent framework for cfd","venue":null,"work_id":"c6915907-dafa-4425-9e6e-710fe77a776c","year":2024},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:50.418371Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:14143aa2b9a40a733b9c7689946d02b3ffff1fbaa5166f21b27a3a7e64711f0c","observation_id":"13569cd7-bf2a-4330-9f04-38dcb19e6936","resolution":{"observed_at":"2026-08-05T16:18:58.836302Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:58.390422Z","title":"Metaopenfoam 2.0: Large language model driven chain of thought for automating cfd simulation and post-processing.Journal Name, 2025","venue":null,"work_id":"b49ff850-f239-42d5-a61c-5770f31fe7b0","year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:50.512003Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:e83f9a327f38dda87a6604625ede0ef417bf66b1a02918828623ec44bcbc208a","observation_id":"0bc953ed-07cc-427a-a39b-5e7ef071b777","resolution":{"observed_at":"2026-08-05T16:18:58.515670Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:58.073121Z","title":"Chatcfd: An llm-driven agent for end-to-end cfd automation with domain-specific structured reasoning","venue":null,"work_id":"aa41d43d-bf9c-4bd7-92f3-b4294a82991b","year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:50.674667Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:ee0d11fd327fbebe035eca1f1d99bac29f594a43a00596c31eb7f106862f8e38","observation_id":"547f7227-6cdf-433c-b8d5-ac50d72d7b32","resolution":{"observed_at":"2026-08-05T16:18:58.189970Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:57.739343Z","title":"Fine-tuning a large language model for automating com- putational fluid dynamics simulations.Theoretical and Applied Mechanics Letters, 15:100594, 2025","venue":null,"work_id":"fdca2660-761d-43fd-9a31-23f553bad798","year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:50.765431Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:8858214a4cf1c848382708991ed39732955740d55d81a370fa53e30f469bd57c","observation_id":"0f910363-9428-4298-95ed-8357aeaac612","resolution":{"observed_at":"2026-08-05T16:18:57.932209Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:57.407426Z","title":"Physics simulation capabilities of llms.Physica Scripta, 99(11):116003, oct 2024","venue":null,"work_id":"7f1520e3-6160-4aa9-b228-9b750776b9f2","year":2024},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:50.820759Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:1bdfdddefaa0e780383a113a350be05fc696c5afacda04e5b22b235d625be5af","observation_id":"49e9db7a-ed9f-476c-9319-cfab14e856ae","resolution":{"observed_at":"2026-08-05T16:18:57.539936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:57.247078Z","title":"Mycrunchgpt: Achatgptassistedframeworkforscientificmachinelearning.Journal of Machine Learning for Modeling and Computing, 4(4):41–72, January 2023","venue":null,"work_id":"86f98412-1898-4ed1-beb4-4ab8e3514477","year":2023},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:50.976305Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:0a59f8bdfe1fa048e8917d71a0d5989cac4465c31d6950caa6a526ab51a72297","observation_id":"236a9a0e-1f1a-4c77-a067-3bdd33a5e057","resolution":{"observed_at":"2026-08-05T16:18:57.323653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12053","last_updated":"2025-01-21T11:26:02Z","snapshot_observed_at":"2026-08-06T17:20:37.861354Z","submitted_at":"2025-01-21T11:26:02Z","title":"PINNsAgent: Automated PDE Surrogation with Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12053","snapshot_observed_at":"2026-08-05T16:18:51.098372Z","title":"Pinnsagent: Automated pde surrogation with large language models.arXiv preprint arXiv:2501.12053, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:51.098372Z"},"links":{"cited_paper":"/paper/2501.12053","citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:4ed56c177c49ea36beb75b0a19c72f4c565e55693ba99f2e80f74029db400220","observation_id":"455538be-7430-4df8-86fa-fbf6ac4152c0","resolution":{"observed_at":"2026-08-05T16:18:51.098372Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:57.061379Z","title":"Lang-pinn: From language to physics-informed neural networks via a multi-agent framework","venue":null,"work_id":"24d09abb-ca79-4597-bb1a-c1d901c5af0f","year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:51.172354Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:321bc3c55a3fc55eaf25dcdb5b0aa805a0894ce6c5cdb61ea328f1cceb6d176b","observation_id":"5747acb2-9304-4dc4-9e47-d1722877ccce","resolution":{"observed_at":"2026-08-05T16:18:57.163703Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:51.249520Z","title":"Text-trained llms can zero-shot extrapolate pde dynamics.arXiv preprint arXiv:2509.06322, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:51.249520Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:a0d4224bf359e42edeed2a2fdea8c3b90b58a774e643f4fefb56046c34a507f2","observation_id":"0086d22d-09aa-4000-8715-371b83c016f6","resolution":{"observed_at":"2026-08-05T16:18:51.249520Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:56.794931Z","title":"Unisolver: Pde- conditional transformers towards universal neural pde solvers","venue":null,"work_id":"f6dd2685-8b4a-45a2-8e80-abf174bcdad7","year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:51.364379Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:ae867a7266f72705266a5423f011e0ef42a7c376ce11a23450afd03b9abcf7c3","observation_id":"d4aba927-5b7f-480c-8cca-e298aeca1816","resolution":{"observed_at":"2026-08-05T16:18:56.929600Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:56.549189Z","title":"UPS: Efficiently building foundation models for PDE solving via cross-modal adaptation.Transactions on Machine Learning Re- search, 2024","venue":null,"work_id":"4a1339f5-70a8-40ee-8648-817d36fd674b","year":2024},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:51.436838Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:eb44c5d93408c999c213b208f483885fab8a43cbc45a2ec3eb0951380ebb4945","observation_id":"a639cd34-ae5e-4074-9efd-a8ab7ee35ed6","resolution":{"observed_at":"2026-08-05T16:18:56.693449Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:56.402582Z","title":"Fluid-llm: Learning computational fluid dynamics with spatiotemporal-aware large language models","venue":null,"work_id":"dbf7fa0e-35b0-42bf-bac0-d339e6a5aa1e","year":2024},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:51.520737Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:25d4e15344865e72f6dd23ef33a76185f7af6681a5582ed496d74394197a486d","observation_id":"c81484b8-6a00-4359-a6ca-242f2e052f02","resolution":{"observed_at":"2026-08-05T16:18:56.479753Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:56.159737Z","title":"Buchanan, and Amir Barati Farimani","venue":null,"work_id":"f8ec7895-1d3a-461b-b9c1-08187536ad2a","year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:51.601218Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:60ae4e8956b21238db175cad02a121afeb17daf9578018309a43c57510896c91","observation_id":"7e0e2556-fe6e-418a-992f-066d32979b30","resolution":{"observed_at":"2026-08-05T16:18:56.255200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:55.942922Z","title":"Yang, Zulfikhar A","venue":null,"work_id":"5ea0de59-3e4f-4a58-8edc-a8f0f0c6506b","year":2023},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:51.678928Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:3f8c41cbbca437ad5f477c45febe2b7d549846b94d01689916928658566fddc2","observation_id":"9cafb488-3202-4ee5-8e7d-fe8052741509","resolution":{"observed_at":"2026-08-05T16:18:56.058706Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:55.712278Z","title":"Aeroagent: A vision- physics-decision framework for aerodynamic vehicle design","venue":null,"work_id":"9c1836bf-fc69-40d3-9194-315949d4e496","year":2026},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:51.774246Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:59f6e45c7a6e72125c6db37db4613a77dd6bc45e0ed421f8a54746475be5f2b1","observation_id":"dd340a43-ab67-46ea-9b66-d8d1103462d6","resolution":{"observed_at":"2026-08-05T16:18:55.815362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:55.485038Z","title":"Shapebench: A scalable benchmark and diagnostic suite for standardized evaluation in aerodynamic shape optimization, 2026","venue":null,"work_id":"f821a878-5bf6-4f89-b638-a9df8a57f42d","year":2026},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:51.831800Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:643c04fa68476b643d5786cda15135ca6c2819b23d10f263b3bee34b49460e5d","observation_id":"6f59649c-4161-4ae6-b0d0-82fcf923e5dc","resolution":{"observed_at":"2026-08-05T16:18:55.579112Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:55.259555Z","title":"Optmetaopenfoam: Large language model driven chain of thought for sensitivity analysis and parameter optimization based on cfd.Journal Name, 2025","venue":null,"work_id":"0eefafc9-2380-4078-b821-6c2845c77642","year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:51.902578Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:0d0389b45b244bba20280ac5e4184cecdfcb20f35782d7cc81ebb35bc7ef1e48","observation_id":"9ff55fb5-62cf-423c-a553-a8a956e838f2","resolution":{"observed_at":"2026-08-05T16:18:55.315190Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:55.145616Z","title":"Self-evolving scientific agent discovers generalizable physically-reasoned fluid control, 2026","venue":null,"work_id":"bbc00171-0b04-45c7-8d65-0cef66bd154c","year":2026},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:52.131193Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:64b681988b46d5dea3f6dda561bcbef2997337ce36bdd7e737899cc6109c3786","observation_id":"ad476b8b-0045-4dba-9ff8-dfe54c64fd24","resolution":{"observed_at":"2026-08-05T16:18:55.183166Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:54.956972Z","title":null,"venue":null,"work_id":"99e4d2ad-f958-4f56-b5d5-26910922cd01","year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:52.236515Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:e3cb2ea8c48714c9a72ec4f9a2d05e22d4f3c81176cc2a562359cc5b5bacfb89","observation_id":"53dc8168-9a24-494b-b282-6d35dcfb4d36","resolution":{"observed_at":"2026-08-05T16:18:55.023199Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:54.782242Z","title":"Toward knowledge-guided ai for inverse design in manufacturing: A perspective on domain, physics, and human–ai synergy","venue":null,"work_id":"1d284600-0576-48c7-b4c4-7407a52d63f8","year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:52.313152Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:77567cb23cdb28518f6cad516e6be2cb352d5dbdce8316ec4f56c05209573dab","observation_id":"6931d88c-f34c-4ad8-8abb-a9a4cbf7a99d","resolution":{"observed_at":"2026-08-05T16:18:54.878610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:54.602920Z","title":"Toward autonomous engineering design: A knowledge-guided multi-agent framework, 2025","venue":null,"work_id":"4d31dacd-5e05-4f5a-8612-9209b203b46c","year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:52.367994Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:51ccf6b3fb6358da18cf32028628afbb1ffb29aa35875c156adac4ba3052505f","observation_id":"50de0bdf-5b50-4afd-b613-33320c091578","resolution":{"observed_at":"2026-08-05T16:18:54.694853Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:54.440271Z","title":"Think like a scientist: Physics-guided llm agent for equation discovery, 2026","venue":null,"work_id":"185b0c57-9ac8-4b28-9744-0c048de55cc3","year":2026},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:52.459872Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:963a4cfd6aa2a435458d01ed291abf983444330b7f002be8cf8e3a318a8af124","observation_id":"ed6c9d42-ed0b-4fee-9742-5f671e080e9f","resolution":{"observed_at":"2026-08-05T16:18:54.511583Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:54.250290Z","title":"Callaghan, and Dongxiao Zhang","venue":null,"work_id":"ef92f4b2-14d1-4570-9634-5b4730b840e7","year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:52.542554Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:7abbe4c5ef419114b42af5786832174911fdf53aeeaa7d8f70f8f76daf70e001","observation_id":"77a8e1e8-4d1b-4e5a-90f3-66c1c95183dc","resolution":{"observed_at":"2026-08-05T16:18:54.322809Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:54.051217Z","title":"Osher, and Hayden Schaeffer","venue":null,"work_id":"4acf2410-9364-436e-8a27-8d487fef901f","year":2025},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:52.615274Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:7085aaba0d9e12cddf8dfda5d39ce23d9c383850c96f50a8096ba9813f5f4634","observation_id":"f8f932cc-cda0-4ea0-b559-c23d2d5ff6f9","resolution":{"observed_at":"2026-08-05T16:18:54.154310Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:53.907833Z","title":"Jasak, A","venue":null,"work_id":"0162df9b-e2be-44f9-a473-58f83327e2bc","year":2007},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:52.676379Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:62399b0df76bb3c9f5bd4d61bf5a1f40f82bef263ab4a526c1550c2cfafb4802","observation_id":"0f5d9b7b-91b0-4132-aa19-3e672cb763aa","resolution":{"observed_at":"2026-08-05T16:18:53.974237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:53.747609Z","title":"PDEBench: AnExtensiveBenchmarkforScientificMachine Learning","venue":null,"work_id":"0fc20f38-eee0-4468-a6de-2d9ee0d67276","year":2022},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:52.737499Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:1022485391492ef026d6766aea8ea2e9abe8d2f284c63836f01c0347f1b97817","observation_id":"a00397e1-51b4-4ce8-a7a3-dd5d6610f228","resolution":{"observed_at":"2026-08-05T16:18:53.804186Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T16:18:53.602607Z","title":"Pinnacle: a comprehensive benchmark of physics-informed neural networks for solving pdes","venue":null,"work_id":"8b6af2e6-400b-4396-a1c4-572c9477b201","year":2024},"citing_paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:52.852949Z"},"links":{"citing_paper":"/paper/2608.03600"},"observation_digest":"sha256:15d208b3362ae53db36d73ca2fe43791f98291042f02064d54368e16838f533b","observation_id":"f07c5078-2ba8-492e-84db-3a5ba6626006","resolution":{"observed_at":"2026-08-05T16:18:53.674753Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2608.03600","last_updated":"2026-08-04T12:51:32Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-07T23:12:09.695523Z","submitted_at":"2026-08-04T12:51:32Z","title":"Large language models for partial differential equation workflows"},"reference_resolution":{"displayed":91,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":31,"verified_exact":1,"verified_fuzzy":59},"total_outbound_references":91},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 91 of 91 outbound references and 0 inbound Pith citation observations for arXiv:2608.03600."}