{"as_of":"2026-08-09T09:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e1049972ac7133902e274e4422e63c7a7a580748ee7844aee42fee049b92683b","coverage":[{"denominator":75,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":75,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T19:39:40.433711Z","state":"measured"},{"denominator":76,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":76,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T18:18:02.185287Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T18:18:09.154780Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"cited_work":{"arxiv_id":"2502.05372","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.05372","snapshot_observed_at":"2026-08-06T18:18:09.154780Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","venue":"cs.LG","work_id":"f0008b96-e0cf-413f-971b-54c81e598bf4","year":2025},"citing_paper":{"arxiv_id":"2507.08749","last_updated":"2025-07-11T16:59:27Z","snapshot_observed_at":"2026-08-06T18:07:26.000754Z","submitted_at":"2025-07-11T16:59:27Z","title":"Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T18:18:02.185287Z"},"links":{"cited_paper":"/paper/2502.05372","citing_paper":"/paper/2507.08749"},"observation_digest":"sha256:9c9f94f28d3286870b3833201b58ddf71ac7c3017f296d860fb4eb4df8feb3c7","observation_id":"d552e5ae-b5d6-40ec-8b6b-44158288af8b","resolution":{"observed_at":"2026-08-06T18:18:09.251956Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2502.05372/citation-record","integrity":"/paper/2502.05372/integrity","json":"/paper/2502.05372/citation-record.json","paper":"/paper/2502.05372"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:39:41.867758Z","title":"Turbulence modeling in the age of data","venue":null,"work_id":"fcdeea50-4b33-4004-9119-c17c6a08fdbd","year":2019},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:39.939358Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:d65df56b08138746b8ca009eedcd93170040a51d0224b9dfb11ed96635ec33d2","observation_id":"d8b9deaf-12c5-4802-829a-d50f8e702833","resolution":{"observed_at":"2026-08-08T19:39:41.873463Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.848828Z","title":"Mont ´ans, Francisco Chinesta, Rafael G ´omez-Bombarelli, and J","venue":null,"work_id":"b5d59c8a-c4e5-4b85-8951-84d9ea31d864","year":2019},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:39.946530Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:1e83ee793844c6ad7f19a525c0585628492df7d2656bb2746497be26d4c3e78a","observation_id":"80798905-3a3f-4887-9b7d-5c5ce6e01c56","resolution":{"observed_at":"2026-08-08T19:39:41.854073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:39.951758Z","title":"Machine learning for fluid mechanics","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:39.951758Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:bc69b178c0c8e271f3cfdd0fb045a7b0c62bea522e40460764e788d754f5b5fc","observation_id":"f1ed53e9-9945-4843-b1bf-94c3e8a7eb6a","resolution":{"observed_at":"2026-08-08T19:39:39.951758Z","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":"2026-08-08T19:39:39.956880Z","title":"Dynamic mode decomposition: data-driven modeling of complex systems","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:39.956880Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:5154724c06fc47e67e1eb0deec4cd2eb6749643a1693b976552aa034cb286101","observation_id":"64285c8c-e827-41a9-924d-f2edc6d53049","resolution":{"observed_at":"2026-08-08T19:39:39.956880Z","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":"2026-08-08T19:39:41.801581Z","title":"Data-driven operator inference for nonintrusive projection-based model reduction","venue":null,"work_id":"9f87be1a-9070-4647-a4e7-7893f5ef9597","year":2016},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:39.962224Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:804a201ebc2686278d7f2cec582080517955dea2926404f7db4cfc770d3c6adb","observation_id":"c8fdecde-8bc9-4461-b026-13b54d7f674a","resolution":{"observed_at":"2026-08-08T19:39:41.807554Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.784489Z","title":"Physics-informed machine learning approach for reconstructing reynolds stress modeling discrepancies based on dns data.Physical Review Fluids, 2(3):034603, 2017","venue":null,"work_id":"b9dda644-97cb-4dce-84f7-6aa3bfe78fdd","year":2017},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:39.966814Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:26f699885357f5492465930a8af1e8dc2438bcb832d0ecafa728754b7aa95dcb","observation_id":"3c46bbf5-7c0f-4b03-a54c-886600d79b86","resolution":{"observed_at":"2026-08-08T19:39:41.790090Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.767525Z","title":"Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework","venue":null,"work_id":"de05e441-78c6-4a85-9de1-fe8a90e8d89e","year":2018},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:39.971838Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:aaf7584b180892134720c5c52734463891253c8691d4c3124a37de8c7640df13","observation_id":"012b998c-6c74-46b9-bcc4-e612e0ae898d","resolution":{"observed_at":"2026-08-08T19:39:41.773187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:39.977397Z","title":"Physics-informed neural net- works: A deep learning framework for solving forward and inverse problems involving non- linear partial differential equations","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:39.977397Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:6a43d8f2aaee1b64f643aef13981ffaf1c156b5f019da72c8e35e8a981fdb758","observation_id":"e62895ba-1f26-4c49-a74e-143e85b5d066","resolution":{"observed_at":"2026-08-08T19:39:39.977397Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.08895","last_updated":"2021-05-17T03:12:33Z","snapshot_observed_at":"2026-07-06T10:05:26.653366Z","submitted_at":"2020-10-18T00:34:21Z","title":"Fourier Neural Operator for Parametric Partial Differential Equations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.08895","snapshot_observed_at":"2026-08-08T19:39:39.981985Z","title":"Fourier neural operator for parametric partial dif- ferential equations","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:39.981985Z"},"links":{"cited_paper":"/paper/2010.08895","citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:dc5ea761dceae4a85ed7dfbd535054cb8e22f90a8e69fed4f314ad986b52e28c","observation_id":"b7a01f26-6151-4fab-9420-b3cb85fb5f1d","resolution":{"observed_at":"2026-08-08T19:39:39.981985Z","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":"2026-08-08T19:39:41.741699Z","title":"Learn- ing nonlinear operators via deeponet based on the universal approximation theorem of oper- ators","venue":null,"work_id":"1f6ab459-7e95-43c9-b37c-67297eef908a","year":2021},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:39.986876Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:dcff9e73beae789d91f216dca94cc60bc9156b92eb11bfec860bd20572a6df80","observation_id":"5be174ec-9fd1-4fa8-aff1-3e0392f43330","resolution":{"observed_at":"2026-08-08T19:39:41.746683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:39.991726Z","title":"Machine learning–accelerated computational fluid dynamics","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:39.991726Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:03bb96a97e64c8a4ed680a72f9247074dc0b301c3c108fcf2177ea75611c39ae","observation_id":"1c4d7f7b-a67e-4e87-80bc-f381657221d0","resolution":{"observed_at":"2026-08-08T19:39:39.991726Z","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":"2026-08-08T19:39:41.714461Z","title":"A causality-based learning approach for discovering the un- derlying dynamics of complex systems from partial observations with stochastic parameteri- zation","venue":null,"work_id":"9e0fa016-b080-451a-bded-5ffb68580972","year":2023},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:39.996740Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:4036005d7c3b7ba4a7a5354a43abc49ee10baee09edbfcc978b188026a6a1923","observation_id":"5fe86554-bf5c-4cae-8920-14851a99cc33","resolution":{"observed_at":"2026-08-08T19:39:41.719556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.697671Z","title":"CEBoosting: Online sparse identification of dynamical systems with regime switching by causation entropy boosting","venue":null,"work_id":"fd2b5683-3e00-48e4-b49f-c8362d15ba56","year":2023},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.003516Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:773a4d8f52db4e3be06b46633d803270081e2efe21055233a745acedb16b4539","observation_id":"37147ca3-cf1f-4def-bca4-f73b77a3968f","resolution":{"observed_at":"2026-08-08T19:39:41.703234Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:40.009174Z","title":"CGNSDE: Conditional Gaussian neural stochastic differential equation for modeling complex systems and data assimilation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.009174Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:d80afb27402f73c1686ee879666234fb3812e50b13e3c44e604be10aa6dc12c6","observation_id":"b36b8740-51e1-4b2d-a4da-eb23bb450a06","resolution":{"observed_at":"2026-08-08T19:39:40.009174Z","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":"2026-08-08T19:39:41.671925Z","title":"CGKN: A deep learning frame- work for modeling complex dynamical systems and efficient data assimilation","venue":null,"work_id":"e638bb2d-fee8-4958-9add-2dd28961a266","year":2025},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.013979Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:bb324357545acadab5d75be55342dbbaf166dad33ae1d399867ed63b9952bdf4","observation_id":"b11d556b-cd28-48e7-b7f4-4f88ff249781","resolution":{"observed_at":"2026-08-08T19:39:41.676870Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.656674Z","title":"Modeling partially observed nonlinear dynamical systems and efficient data assimilation via discrete-time conditional gaussian koopman network","venue":null,"work_id":"e91f3784-f21f-488d-a6e5-e2ba43ffb2e0","year":2025},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.019493Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:2bcb1ac6a19fdf944bc0004ea94d24a31878b5e03845aba18f10a410d830152a","observation_id":"a3900abe-03aa-46a4-b146-f4c989897c46","resolution":{"observed_at":"2026-08-08T19:39:41.661544Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:40.024298Z","title":"Atkinson and William G","venue":null,"work_id":null,"year":1968},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.024298Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:ae0ebd0375ef8a4962e77c9517b09b70af9b1677e58076edbc944ea4df27070d","observation_id":"d06cf25c-044c-427a-b4c9-1183de01b4ba","resolution":{"observed_at":"2026-08-08T19:39:40.024298Z","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":"2026-08-08T19:39:41.641767Z","title":null,"venue":null,"work_id":"82de881e-911d-40f9-819a-7bd2ed8ead46","year":1992},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.029640Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:270a13bd8c8b40ff62d7a66c9889c75d117cef626fb4fd6b2a55d577c9228179","observation_id":"e6b37222-eb0a-440b-be93-5ed1ec2c19c6","resolution":{"observed_at":"2026-08-08T19:39:41.646226Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.626268Z","title":null,"venue":null,"work_id":"4c5dc372-2c5c-4921-a0de-d1aba7f178c8","year":null},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.034380Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:39515eb3347138b49c500716f6ea8ebe024b90f99ff57aa8984a38783a94ae1d","observation_id":"5f570505-3fd5-4c19-bbda-524bd288b2b8","resolution":{"observed_at":"2026-08-08T19:39:41.631020Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.609110Z","title":null,"venue":null,"work_id":"b809080e-61a0-41e5-b64c-8153afbbc0ee","year":null},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.039533Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:2394437babf85bc1eb57986de9a08469de712c16840cd1ba36127330d341b570","observation_id":"99031091-b2aa-43d9-9c46-ceeed5a5645f","resolution":{"observed_at":"2026-08-08T19:39:41.615408Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.589124Z","title":"Ryan, Christopher C","venue":null,"work_id":"4122f02c-448d-46eb-9f97-1eaffc55ebac","year":2016},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.044341Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:2d5a2e26a52f6b8f4191d05d0369fe05f77bf79b2b25d9c596b7948d60a25f45","observation_id":"a4dae214-93c0-4b70-9237-be9db33f457a","resolution":{"observed_at":"2026-08-08T19:39:41.595120Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.571510Z","title":"Bayes linear analysis for Bayesian optimal experimental design","venue":null,"work_id":"14840751-06ff-41e5-9e8b-e8bf4e5d3280","year":2016},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.049510Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:a1a40b7e6ca0cd876877c707ddbda9fb50cd8e5d1524004cd1d3edeb7ee70510","observation_id":"0572e9a1-b0f4-458d-8787-620409a8aaf0","resolution":{"observed_at":"2026-08-08T19:39:41.576363Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.556148Z","title":null,"venue":null,"work_id":"cd52010a-177d-4998-ab3f-0021cb9da55a","year":2013},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.054257Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:c6e453fe22b3b660733af75dae65453824e6480d5b49d48f25f971ced5a1eafa","observation_id":"94eae0b9-79ab-4066-9b29-162d119b293b","resolution":{"observed_at":"2026-08-08T19:39:41.561193Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.14545","last_updated":"2023-11-29T10:20:19Z","snapshot_observed_at":"2026-08-05T23:54:23.242967Z","submitted_at":"2023-02-28T13:10:04Z","title":"Modern Bayesian Experimental Design","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.14545","snapshot_observed_at":"2026-08-08T19:39:40.059110Z","title":"Ivanova, and Freddie Bickford Smith","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.059110Z"},"links":{"cited_paper":"/paper/2302.14545","citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:705252ead0e679f52fae5f1966ae092305d70f451cfdd1051901a90484d0215a","observation_id":"d7a89bb3-330a-4cd0-8e37-f68f6215f94b","resolution":{"observed_at":"2026-08-08T19:39:40.059110Z","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":"2026-08-08T19:39:41.540446Z","title":"Applied Statistical Decision Theory","venue":null,"work_id":"2a03a4bc-0ba9-43b2-a1bd-d689d63e27ec","year":2000},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.063925Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:dc23c8177f1f611b63817c7fa7c008e5ca8d1a43c2b059c6c0bc6bad45055e9a","observation_id":"0146a1c4-fb45-4528-8ffc-8508ed9db89e","resolution":{"observed_at":"2026-08-08T19:39:41.545774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.517399Z","title":null,"venue":null,"work_id":"53636f48-c3b0-4529-b7a2-0ddfa962bd80","year":2005},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.069411Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:733d1b91eeaed3cc3927c1c98cf97ddeb1faad609f66aa0763e4cd3727c6e6fc","observation_id":"5e4409c6-e9a9-4bba-9d2b-d906bdcbaefb","resolution":{"observed_at":"2026-08-08T19:39:41.529134Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.494389Z","title":null,"venue":null,"work_id":"fba7e64f-96eb-4c67-a91b-85877a120fa8","year":1972},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.074260Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:255ea138dccfbca4380dfcad8314eb5e7be45d70c022af7d7fb47e6cedb0f123","observation_id":"30e1c6cd-c931-45de-bf81-1f43021e882e","resolution":{"observed_at":"2026-08-08T19:39:41.500507Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.477412Z","title":"Bayesian Experimental Design: A Review","venue":null,"work_id":"049406e5-17ab-4e84-bbc3-ad6d7abab9b7","year":1995},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.079345Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:1cbacf56a3a68b68fb1d8fa6947e0d7813790364343f74901df45383330c0492","observation_id":"43932105-06bc-4d64-b37f-11f9fadd8b24","resolution":{"observed_at":"2026-08-08T19:39:41.483150Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/sim.4780140904","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:39:40.469309Z","title":"Bayesian Decision Procedures for Dose De- termining Experiments","venue":null,"work_id":"c9402da4-fe9c-41cf-909f-3c5df0cf6fce","year":1995},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.083952Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:2450638f7df18347f5fc2c703f0ec6dbd365a9420e503bfe53db4e15470aa9d9","observation_id":"e04b8caf-893b-4664-a485-94fa56fbb0e9","resolution":{"observed_at":"2026-08-08T19:39:40.476268Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.461494Z","title":"Cavagnaro, Jay I","venue":null,"work_id":"553f6514-1926-48b9-a804-f3a2e3f432e5","year":2010},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.089189Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:c99b23c6a48e7ed073b46c666b6ba469873d5f0c7f0372c8d8deb5ed66ff81e7","observation_id":"2f8135d0-d9ad-4e7f-aa2c-14525529d1ab","resolution":{"observed_at":"2026-08-08T19:39:41.466519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.445853Z","title":"Drovandi, James M","venue":null,"work_id":"36c27690-4c4e-46a2-8017-7454dacb1f0b","year":2013},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.093634Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:edd8ce19c395b52802849370136adf725170d7d7bfdf798f4e8901e15576c5bf","observation_id":"c8e57bcb-16f8-41a0-86d7-22d4614f04f7","resolution":{"observed_at":"2026-08-08T19:39:41.450419Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.02329","last_updated":"2021-11-03T16:24:05Z","snapshot_observed_at":"2026-07-06T12:05:04.503896Z","submitted_at":"2021-11-03T16:24:05Z","title":"Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods","version":1},"cited_work":{"arxiv_id":"2111.02329","doi":null,"metadata_source":"pith","pith_arxiv_id":"2111.02329","snapshot_observed_at":"2026-08-08T19:39:40.800230Z","title":"Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods","venue":"stat.ML","work_id":"909fe09b-65d9-46ca-8cd3-b87649c34cb8","year":2021},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.099044Z"},"links":{"cited_paper":"/paper/2111.02329","citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:bfcf8899cec505c7006fb199870cfbbea26aa14037037173803b5d0c4f219145","observation_id":"bbaa350f-b642-489c-88f9-4e788881eaf8","resolution":{"observed_at":"2026-08-08T19:39:40.805797Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.02438","last_updated":"2021-06-11T12:18:18Z","snapshot_observed_at":"2026-07-06T10:46:27.754260Z","submitted_at":"2021-03-03T14:43:48Z","title":"Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design","version":2},"cited_work":{"arxiv_id":"2103.02438","doi":null,"metadata_source":"pith","pith_arxiv_id":"2103.02438","snapshot_observed_at":"2026-08-08T19:39:40.777795Z","title":"Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design","venue":"stat.ML","work_id":"4f80eb2b-825a-483e-bf49-befda83bb204","year":2021},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.104546Z"},"links":{"cited_paper":"/paper/2103.02438","citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:513065d95914826a8bd1d7043f59f4bef7f5b9099d32610d00accb2587596b60","observation_id":"5e9fa039-d407-4527-ad16-4aed415917b5","resolution":{"observed_at":"2026-08-08T19:39:40.782933Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1212.2228","last_updated":"2014-12-26T22:54:32Z","snapshot_observed_at":"2026-07-06T03:01:40.911696Z","submitted_at":"2012-12-10T21:47:11Z","title":"Gradient-based stochastic optimization methods in Bayesian experimental design","version":3},"cited_work":{"arxiv_id":"1212.2228","doi":null,"metadata_source":"pith","pith_arxiv_id":"1212.2228","snapshot_observed_at":"2026-08-08T19:39:40.755074Z","title":"Gradient-based stochastic optimization methods in Bayesian experimental design","venue":"stat.CO","work_id":"e770b7be-a230-4e76-92c8-00ebb45b80aa","year":2012},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.109733Z"},"links":{"cited_paper":"/paper/1212.2228","citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:0975d351d329c9c5811fb07bdbf9f9c878d3a2c4c0fe81cf4a0ecc44167d708c","observation_id":"874d985a-78ee-46cf-8c5b-3dc375fd7366","resolution":{"observed_at":"2026-08-08T19:39:40.760356Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.15335","last_updated":"2022-03-20T19:19:16Z","snapshot_observed_at":"2026-08-08T02:03:17.133731Z","submitted_at":"2021-10-28T17:47:31Z","title":"Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.15335","snapshot_observed_at":"2026-08-08T19:39:40.115786Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.115786Z"},"links":{"cited_paper":"/paper/2110.15335","citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:34766d6d8f31855f6df4ea069c8de2fb46cfb2b0c881d3d1c3a18bebc9d6373c","observation_id":"870bc0b3-26c2-4679-81a6-97958f1d1376","resolution":{"observed_at":"2026-08-08T19:39:40.115786Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.16212","last_updated":"2026-04-29T20:39:22Z","snapshot_observed_at":"2026-08-07T16:55:12.999376Z","submitted_at":"2024-07-23T06:33:37Z","title":"Optimal experimental design: Formulations and computations","version":2},"cited_work":{"arxiv_id":"2407.16212","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.16212","snapshot_observed_at":"2026-08-08T19:39:40.716089Z","title":"Optimal experimental design: Formulations and computations","venue":"stat.ME","work_id":"4f2b8ad7-cf5e-46a5-b2f3-21615d700c17","year":2024},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.121921Z"},"links":{"cited_paper":"/paper/2407.16212","citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:3d2400e104bdbb55388ec81763fcee8697d59e943b7e89efaaf65d8a0134c39d","observation_id":"4ad62aac-ce4f-4e64-ba5d-b64c39067d97","resolution":{"observed_at":"2026-08-08T19:39:40.721719Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.427836Z","title":"Berry, Andy P","venue":null,"work_id":"f7569095-a0d5-4346-8e8d-c95b358a914d","year":2007},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.127737Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:e2f28bff102fa0c8f90d2de9516607fe1d26d917303a5b19c7472eb9659012f1","observation_id":"656bddb3-7985-44fc-8ccc-d3238fffb7cc","resolution":{"observed_at":"2026-08-08T19:39:41.433620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.412351Z","title":"Bayesian inference in physics","venue":null,"work_id":"5f63934b-6ee5-42c7-ac78-b00def46442a","year":2011},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.133683Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:d1d3f5469095023ca77eb47f897b26f0561df002a16054c279dadfcde08aa2c7","observation_id":"2a00f1f0-6dca-4ce5-9e74-e7a6375c4bf5","resolution":{"observed_at":"2026-08-08T19:39:41.417879Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.396541Z","title":"Numerical approaches for sequential Bayesian optimal experimental design","venue":null,"work_id":"2ecc61a3-e315-4540-829a-8329b94749a3","year":2015},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.139177Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:2992d62184d745584e7b797ded726211c009a63bfd233b18b3df059533128475","observation_id":"6b5aa291-9afd-448c-af0d-371e0d434daa","resolution":{"observed_at":"2026-08-08T19:39:41.402194Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.380886Z","title":null,"venue":null,"work_id":"e322e950-434c-49f5-9448-3c1c1923a056","year":1992},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.143734Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:06ab78ddd8b158cc01c4f230c37ee1c5ea52ba9d73f66a3a0f69384149788527","observation_id":"da6b9967-3177-414e-a6a9-ef98c0f72ed6","resolution":{"observed_at":"2026-08-08T19:39:41.385510Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.365626Z","title":"Drovandi, James M","venue":null,"work_id":"7b0e804a-ffdd-4e92-b02b-86d56ab64fb7","year":2014},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.149032Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:d421512321bb13b7fda75a8a7430df466f1d7a9149748da0b5d56ad64d0a244a","observation_id":"56ed4d2f-cd6f-439e-945e-7ee53205bb1a","resolution":{"observed_at":"2026-08-08T19:39:41.370253Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.09379","last_updated":"2020-03-20T16:52:10Z","snapshot_observed_at":"2026-07-06T09:06:13.430067Z","submitted_at":"2020-03-20T16:52:10Z","title":"Sequential Bayesian Experimental Design for Implicit Models via Mutual Information","version":1},"cited_work":{"arxiv_id":"2003.09379","doi":null,"metadata_source":"pith","pith_arxiv_id":"2003.09379","snapshot_observed_at":"2026-08-08T19:39:40.692347Z","title":"Sequential Bayesian Experimental Design for Implicit Models via Mutual Information","venue":"stat.ML","work_id":"f5fb933a-6cdb-47ac-ae8e-311ef858db93","year":2020},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.153606Z"},"links":{"cited_paper":"/paper/2003.09379","citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:94eeaaed908228520932e44888c6ebb2b3b076269fa1286b0b80a1b12f750724","observation_id":"565e821e-b533-46a9-81ea-55a62347ff2a","resolution":{"observed_at":"2026-08-08T19:39:40.697923Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1604.08320","last_updated":"2016-04-28T06:32:27Z","snapshot_observed_at":"2026-08-05T03:53:08.617563Z","submitted_at":"2016-04-28T06:32:27Z","title":"Sequential Bayesian optimal experimental design via approximate dynamic programming","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1604.08320","snapshot_observed_at":"2026-08-08T19:39:40.159167Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.159167Z"},"links":{"cited_paper":"/paper/1604.08320","citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:62fd4ab4d096319c37e9692d90cd2923bd07efccaa32c9343bb8011802710399","observation_id":"189b917f-72f2-4776-ab98-e7e7637b4905","resolution":{"observed_at":"2026-08-08T19:39:40.159167Z","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":"2026-08-08T19:39:41.349750Z","title":"Kennedy and Anthony O’Hagan","venue":null,"work_id":"1c9860cb-5514-4c5c-8ac5-be6e02332571","year":2001},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.164606Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:dd8431fa6fabfd75af56353e57204e8deff82a714fb577d5710dd772b4b1d7fb","observation_id":"28d1e9ee-2a7b-4996-9b63-6c8bb8019d9a","resolution":{"observed_at":"2026-08-08T19:39:41.354751Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.334089Z","title":"Inconsistency of Bayesian Inference for Misspeci- fied Linear Models, and a Proposal for Repairing It","venue":null,"work_id":"89f0d5c0-4fa1-43e0-8cbb-46c32b35b81e","year":2017},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.169675Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:fa0cb0ece113ad4891e6d3aff48d911880ad5e33817b0626562db288defeffa6","observation_id":"a08698a7-2f79-4afe-8e44-93577cf66445","resolution":{"observed_at":"2026-08-08T19:39:41.339001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.317914Z","title":"Learning about physical parameters: the impor- tance of model discrepancy","venue":null,"work_id":"e8da47fc-9847-408c-9ef5-4f0d7ba6cb70","year":null},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.174356Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:da9f95b40d197fd343894c46ce4dbb67aa38b95f51cc192df70809727206fb6c","observation_id":"1a6cbb49-1ea3-46b9-a587-199b26e1aa24","resolution":{"observed_at":"2026-08-08T19:39:41.322912Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07949","last_updated":"2023-04-17T02:13:20Z","snapshot_observed_at":"2026-07-06T15:16:17.383715Z","submitted_at":"2023-04-17T02:13:20Z","title":"Metrics for Bayesian Optimal Experiment Design under Model Misspecification","version":1},"cited_work":{"arxiv_id":"2304.07949","doi":null,"metadata_source":"pith","pith_arxiv_id":"2304.07949","snapshot_observed_at":"2026-08-08T19:39:40.653576Z","title":"Metrics for Bayesian Optimal Experiment Design under Model Misspecification","venue":"stat.ME","work_id":"7ad605e9-683d-440e-a4db-b88712b25864","year":2023},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.179090Z"},"links":{"cited_paper":"/paper/2304.07949","citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:be0217dd053ec642f7357ad3f92d01b6b12eb79b5ce86293e8e49e09bcccc2fe","observation_id":"9af680a1-00fc-4419-bc8e-f2c8bb7fe9b4","resolution":{"observed_at":"2026-08-08T19:39:40.659013Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.300968Z","title":"Optimal Bayesian experimental design in the presence of model error","venue":null,"work_id":"ee52895a-8268-467a-9926-6d6ad640685f","year":2015},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.184696Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:bf6e9d9205ef885b91cafe8f29f9fc834900303f166b1d2c2eef44eb60b89e65","observation_id":"e12a7fbb-1f6c-4145-a627-b3d2a039bae1","resolution":{"observed_at":"2026-08-08T19:39:41.306893Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.285588Z","title":null,"venue":null,"work_id":"a660549e-77ca-4d5e-a744-bbf5196d8bfe","year":null},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.189357Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:6483a66f6441656d9cb6e351b82769a2e9612f5f7afd3e5778602b45f302cd0c","observation_id":"74a80dd6-1e2c-4dca-ac2c-0fa82c55ce2c","resolution":{"observed_at":"2026-08-08T19:39:41.290041Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.269694Z","title":null,"venue":null,"work_id":"35b30972-88f4-49a6-9f7d-a22f2b75e3d0","year":2009},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.315550Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:bd953977b5140da8afd99b837855fc6ad8ff915567e969966470fe6da5f514b1","observation_id":"1a06a674-b7ec-4740-8d6e-7217dd319d26","resolution":{"observed_at":"2026-08-08T19:39:41.275580Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.255397Z","title":null,"venue":null,"work_id":"bcd6491f-6b1f-44a8-bae4-23807621319b","year":2014},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.320495Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:1e8766fd289d7483627cda1d066750dd6be9b3d9849e60bd5b3fd3e15ba05047","observation_id":"179494a5-d728-4b78-83f5-393fd01c9099","resolution":{"observed_at":"2026-08-08T19:39:41.259896Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.241112Z","title":"Iglesias, Kody J","venue":null,"work_id":"01fec078-7f15-4e04-afb0-2a5c780cb894","year":2013},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.326191Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:e5a965dc8d87c2da176dd445a6d53090804e300c7382dd59ae185ae76784cb5a","observation_id":"2418e0d9-fec7-4564-ba40-161794b79e66","resolution":{"observed_at":"2026-08-08T19:39:41.245635Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.225549Z","title":"Kovachki and Andrew M","venue":null,"work_id":"e0a29e1d-3f36-496c-af09-3f6da03a5c54","year":2019},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.331675Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:96c3ce32493083db320d63bc38ba83cbfe3edcaf328700886686e4c1d1f2f8d5","observation_id":"b407724e-b8de-4934-9c07-1c96d2e264b9","resolution":{"observed_at":"2026-08-08T19:39:41.229853Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.210208Z","title":"Learning about struc- tural errors in models of complex dynamical systems","venue":null,"work_id":"b1293a72-d49a-4fd3-90cd-61e47be15c05","year":2024},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.336389Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:433efd5136b826ce092a447214b38ff148c93966c0cd73488e2216faaf70a647","observation_id":"ca222757-f880-42b4-8e4d-e7811bb8581d","resolution":{"observed_at":"2026-08-08T19:39:41.215926Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.193913Z","title":"Neural dynamical operator: Continuous spatial-temporal model with gradient-based and derivative-free optimization methods","venue":null,"work_id":"54e7c0af-6504-4880-a7fe-f81e8fa356ea","year":2025},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.341432Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:53267d1c264a31a3c30070071da5ba464504f5c516c98d97972d063a74e497e3","observation_id":"9b170b91-2c08-49b4-b487-9eb300242b7d","resolution":{"observed_at":"2026-08-08T19:39:41.199613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.02965","last_updated":"2025-04-18T21:18:24Z","snapshot_observed_at":"2026-08-07T05:31:10.976162Z","submitted_at":"2024-08-06T05:21:31Z","title":"Data-Driven Stochastic Closure Modeling via Conditional Diffusion Model and Neural Operator","version":3},"cited_work":{"arxiv_id":"2408.02965","doi":null,"metadata_source":"pith","pith_arxiv_id":"2408.02965","snapshot_observed_at":"2026-08-08T19:39:40.513089Z","title":"Data-Driven Stochastic Closure Modeling via Conditional Diffusion Model and Neural Operator","venue":"cs.LG","work_id":"cbfa293f-4b6f-4687-9a7f-ef4f97c5789b","year":2024},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.345913Z"},"links":{"cited_paper":"/paper/2408.02965","citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:ca6e190f9427e389ea4a024b319b1cc57100fa4db3a74b4fcd887dbcb1465899","observation_id":"3b74c719-682a-480a-b354-20b4bef3cda9","resolution":{"observed_at":"2026-08-08T19:39:40.519062Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.20771","last_updated":"2026-06-27T01:54:59Z","snapshot_observed_at":"2026-08-06T22:39:44.436399Z","submitted_at":"2025-06-25T19:04:02Z","title":"Stochastic and Non-local Closure Modeling for Nonlinear Dynamical Systems via Latent Score-based Generative Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.20771","snapshot_observed_at":"2026-08-08T19:39:40.350782Z","title":"Stochastic and non-local closure modeling for nonlinear dynamical systems via latent score-based generative models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.350782Z"},"links":{"cited_paper":"/paper/2506.20771","citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:bce694d155e871afc197e8c08c2b68e3b1af8029c4a5bf0c52b49ca625736310","observation_id":"2c8aa06a-e5b7-47a7-8e5a-b847da1f01c5","resolution":{"observed_at":"2026-08-08T19:39:40.350782Z","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":"2026-08-08T19:39:41.178268Z","title":"Lorentzen, and Tuhin Bhakta","venue":null,"work_id":"a3527fd5-6b24-4cbb-a6a0-6a3606ac3554","year":null},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.355674Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:febea44679158e47fc491675d3b7b7f0ce9159bd4fa557fb5b99f0bd555ba60d","observation_id":"0a17e1ea-b47f-4ea2-ba3b-ec9a18dfe2ce","resolution":{"observed_at":"2026-08-08T19:39:41.183440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.163855Z","title":null,"venue":null,"work_id":"8daa30a6-0daa-4a21-9277-6949db15e539","year":1956},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.360315Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:a35df1dbeb00001e379768b0cc2ceb142e24a47c05ec9e57fc13d6a8df939ebf","observation_id":"400de069-68bd-4740-87c8-19dc77f472f5","resolution":{"observed_at":"2026-08-08T19:39:41.168452Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.148385Z","title":null,"venue":null,"work_id":"41b6822a-3996-492b-9228-98b5a576ba3d","year":null},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.364586Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:4ef20cc33fcfd42953f23f044a7dd5402cf0e22a6ed6223322661795a388603f","observation_id":"113f6bee-80b7-4fca-a023-faea797c7e8f","resolution":{"observed_at":"2026-08-08T19:39:41.154018Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.132701Z","title":"Closed-loop model-based design of experiments for kinetic model dis- crimination and parameter estimation: Benzoic acid esterification on a heterogeneous cata- lyst","venue":null,"work_id":"2b998110-74e3-4e36-a3d2-2c8945d5be7e","year":null},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.370134Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:ecee1bb791991ee6c79484b820642e753cc45fa9768661f0bf8ee59ae775814f","observation_id":"871036fe-72de-4777-a610-9d0cb18d39e2","resolution":{"observed_at":"2026-08-08T19:39:41.138369Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.117418Z","title":"Bernal Neira, and Alexander W","venue":null,"work_id":"cc9c6134-d460-444a-b4df-68fdcdc90587","year":null},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.374658Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:1d4a8ab8c6bbfe96941727d817b58ca45f0c3f36cd6ecb0ede1eb1738ca5a979","observation_id":"275e647b-42cb-490e-8689-081f325d8fa3","resolution":{"observed_at":"2026-08-08T19:39:41.122260Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.100570Z","title":"On automatic differentiation","venue":null,"work_id":"253a72fe-2adc-439b-992b-4a9fdc3608a2","year":1988},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.380087Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:6606954f855b338844817979a1f336367c48864b5e3be89486a02b1bb79d1c04","observation_id":"662048ad-8e1b-411c-9d3e-0fd3304d4536","resolution":{"observed_at":"2026-08-08T19:39:41.105286Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.085108Z","title":"Compiling machine learning pro- grams via high-level tracing","venue":null,"work_id":"6f14025d-b9c8-4e8a-af5c-cf23cbb374d6","year":null},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.384855Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:000ec27bc8c2bd3daf96d6089aef2abca351253bce701b1ba84e3152a01c21ba","observation_id":"56c1c161-ad24-4f49-9191-4fadb77a9cc0","resolution":{"observed_at":"2026-08-08T19:39:41.089888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.069522Z","title":"Methods of Mathematical Physics","venue":null,"work_id":"8e81df91-d9f1-4c5d-b11f-bcb427458d80","year":1985},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.390156Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:3d60dd27ccd187c27d130ca8f94f0a4d071a5da6ecc3057176b18f86c04eb9ab","observation_id":"634e6b90-2138-4944-949c-e6d1ed14e511","resolution":{"observed_at":"2026-08-08T19:39:41.074328Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.054154Z","title":"A tutorial on the adjoint method for inverse problems","venue":null,"work_id":"37f895ca-191a-4a15-b68e-6d5a784e6b7f","year":2021},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.394892Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:0cb6538e2c594c5dfb67dd98b41985e44aebdb456547ad8190338868d287c351","observation_id":"7b2ff26c-35f4-41ba-83ae-ab54c004a54f","resolution":{"observed_at":"2026-08-08T19:39:41.059715Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:41.039338Z","title":"Adjoint sensitivity analysis for differential- algebraic equations: algorithms and software","venue":null,"work_id":"4fb37208-6492-4c16-b2a1-9ff2c5d40e28","year":null},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.399604Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:be4b67e957ae8b647b4f85faa7ff06113689b32f3f39fcdbb5f8c3e1ed9cb30d","observation_id":"1a4cda78-fb97-414a-9e43-4a77eec3a11b","resolution":{"observed_at":"2026-08-08T19:39:41.044219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:40.404447Z","title":"JAX: composable transformations of Python+NumPy programs, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.404447Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:8393a01f87e0fa4b909776e3745ce2443fa6ceafe9e15b003a442321b780da96","observation_id":"9a865d07-4c0e-44fc-9476-08a53bf4e1c7","resolution":{"observed_at":"2026-08-08T19:39:40.404447Z","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":"2026-08-08T19:39:41.012423Z","title":"Smith, Ayya Alieva, Qing Wang, Michael P","venue":null,"work_id":"cdec62ee-6eac-4cb3-8170-bc58984d3928","year":2021},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.409673Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:5370a4ff5d01340e502a110b3bf97a5857b3ece35c41998d47bb8c9309ba91c6","observation_id":"74cd0893-e46a-4e43-92e8-dea3dcb0d7ee","resolution":{"observed_at":"2026-08-08T19:39:41.018121Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:40.994427Z","title":null,"venue":null,"work_id":"1c2c4c1b-2c97-4836-aed0-185108479338","year":2000},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.414435Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:790c746897761e568d86b61375471e40cf789f19f53f24384dd68de8a059bbc2","observation_id":"8b31f15b-552e-4a29-81f7-fba32d8fd3e6","resolution":{"observed_at":"2026-08-08T19:39:40.999417Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:40.979202Z","title":"Methods of Mathematical Physics, Vol","venue":null,"work_id":"557410f7-b138-4ab5-a042-500dd5b95ad4","year":1954},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.419345Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:2e3b31aa15fefe2ac17587fc50732e0de83a6b9649af539653e68a08376c8258","observation_id":"415f3ce5-c9eb-4282-852f-bcec6b651050","resolution":{"observed_at":"2026-08-08T19:39:40.983912Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:40.964822Z","title":"Atkinson","venue":null,"work_id":"afff11c4-d54f-4f80-8d77-5346026690f1","year":2009},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.424069Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:99a54f89d0fbfa1f5ce1816ecbce9ef4be2ce6d4c3e958df54bf0fe18275b27a","observation_id":"8a80df73-7734-49df-826f-b9dbb410b917","resolution":{"observed_at":"2026-08-08T19:39:40.969573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:40.948590Z","title":"Large-scale bayesian optimal experimental design with derivative-informed projected neural network","venue":null,"work_id":"41ebb6b3-0ec0-4aea-869e-73d2b7d4284e","year":null},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.429035Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:ca24c3a2b3779221e8db84c0ffdce9d7bbe386bb64e83792f0266d32944a14ba","observation_id":"cc95aad8-5564-48ce-a70a-8531d067dee7","resolution":{"observed_at":"2026-08-08T19:39:40.954420Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:40.932507Z","title":null,"venue":null,"work_id":"28a96cbb-a768-4eba-bc2a-3fdae22d8083","year":null},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.433711Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:fde2e5970788ec83361ec4101fd07dbcb250ca603e6b13aaf205abb698ed6a12","observation_id":"420fb0e2-b550-4c75-affe-43aca75f0e3f","resolution":{"observed_at":"2026-08-08T19:39:40.938139Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T19:39:40.194436Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.194436Z"},"links":{"citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:957141db09c2ae3e555b66c696330cefd840f4485f7d5a8a1aaf79f908846c03","observation_id":"8573ff30-6975-4487-af89-0e92aea6d9cf","resolution":{"observed_at":"2026-08-08T19:39:40.194436Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T19:34:07.219468Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design"},"reference_resolution":{"displayed":75,"state_counts":{"malformed_identifier":0,"metadata_mismatch":3,"parse_uncertain":0,"unresolved":27,"verified_exact":5,"verified_fuzzy":40},"total_outbound_references":75},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 75 of 75 outbound references and 1 inbound Pith citation observation for arXiv:2502.05372."}