{"as_of":"2026-08-15T20:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b3286c330965d2c2e7deeb0604acc50d07301801c56ae9ffddb092714137d7a9","coverage":[{"denominator":17,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":17,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T12:29:34.686969Z","state":"measured"},{"denominator":17,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":17,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2412.14132/citation-record","integrity":"/paper/2412.14132/integrity","json":"/paper/2412.14132/citation-record.json","paper":"/paper/2412.14132"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:29:35.299756Z","title":"Araz, Juan Carlos Criado, and Michael Spannwosky","venue":null,"work_id":"21f60513-e73c-4cd3-91d5-f460ad861a19","year":2021},"citing_paper":{"arxiv_id":"2412.14132","last_updated":"2024-12-18T18:21:41Z","snapshot_observed_at":"2026-08-15T04:14:05.624395Z","submitted_at":"2024-12-18T18:21:41Z","title":"jinns: a JAX Library for Physics-Informed Neural Networks","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-11T12:29:34.445267Z"},"links":{"citing_paper":"/paper/2412.14132"},"observation_digest":"sha256:6c289b9a84c5cecb265a715672c42836ed5bf7f7d875d2f64d62f3832d3ae7cd","observation_id":"9e17b57c-a0c0-411c-b172-f10d492f6d1d","resolution":{"observed_at":"2026-08-11T12:29:35.315000Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T12:29:35.259921Z","title":"JAX : composable transformations of P ython+ N um P y programs, 2024","venue":null,"work_id":"39ae718d-0d8c-47e0-b18e-8152906b9507","year":2024},"citing_paper":{"arxiv_id":"2412.14132","last_updated":"2024-12-18T18:21:41Z","snapshot_observed_at":"2026-08-15T04:14:05.624395Z","submitted_at":"2024-12-18T18:21:41Z","title":"jinns: a JAX Library for Physics-Informed Neural Networks","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-11T12:29:34.457105Z"},"links":{"citing_paper":"/paper/2412.14132"},"observation_digest":"sha256:6e42e39c7369d4232e99a3cd29f56a3740c6382c1f17babcc5e88ac104167aeb","observation_id":"711cc893-390d-476d-960c-1baad59e6635","resolution":{"observed_at":"2026-08-11T12:29:35.275229Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T12:29:35.220489Z","title":"Separable physics-informed neural networks","venue":null,"work_id":"a2b28c2b-bb21-4898-84a3-ce414829c0f2","year":2024},"citing_paper":{"arxiv_id":"2412.14132","last_updated":"2024-12-18T18:21:41Z","snapshot_observed_at":"2026-08-15T04:14:05.624395Z","submitted_at":"2024-12-18T18:21:41Z","title":"jinns: a JAX Library for Physics-Informed Neural Networks","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-11T12:29:34.471343Z"},"links":{"citing_paper":"/paper/2412.14132"},"observation_digest":"sha256:840acf5de1924cd16e690a9c8c24e9a2d29c9164f267956fd325309a38d47b65","observation_id":"80cdaec1-a2df-4f46-a5a7-c975c149e3bb","resolution":{"observed_at":"2026-08-11T12:29:35.231541Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.21105/joss.05352","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:29:34.772751Z","title":"Physics-informed neural networks for advanced modeling","venue":null,"work_id":"5e399e78-5fa8-44b3-9579-1c2c7ba37d80","year":2023},"citing_paper":{"arxiv_id":"2412.14132","last_updated":"2024-12-18T18:21:41Z","snapshot_observed_at":"2026-08-15T04:14:05.624395Z","submitted_at":"2024-12-18T18:21:41Z","title":"jinns: a JAX Library for Physics-Informed Neural Networks","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-11T12:29:34.480490Z"},"links":{"citing_paper":"/paper/2412.14132"},"observation_digest":"sha256:95d7a3e57ef4181180b7d5754aef6900e6fd6511282872601597811baf65f14b","observation_id":"0e9cf46d-aa9f-4aa5-b699-2c745116d8f3","resolution":{"observed_at":"2026-08-11T12:29:34.782320Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T12:29:35.183301Z","title":"Scientific machine learning through physics--informed neural networks: Where we are and what’s next","venue":null,"work_id":"c0239e28-140a-4add-aaaa-fe06f7169301","year":2022},"citing_paper":{"arxiv_id":"2412.14132","last_updated":"2024-12-18T18:21:41Z","snapshot_observed_at":"2026-08-15T04:14:05.624395Z","submitted_at":"2024-12-18T18:21:41Z","title":"jinns: a JAX Library for Physics-Informed Neural Networks","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-11T12:29:34.500824Z"},"links":{"citing_paper":"/paper/2412.14132"},"observation_digest":"sha256:9f62d6bdf8467e5150cd37799c2b502849b44c693afd77e72c2fabeeef8fc94f","observation_id":"a33f8ec8-497b-4f88-9dc2-63dd6117b865","resolution":{"observed_at":"2026-08-11T12:29:35.195453Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T12:29:35.149368Z","title":"Systems biology: Identifiability analysis and parameter identification via systems-biology-informed neural networks","venue":null,"work_id":"3938107b-1528-4fff-952e-77696b70fd33","year":2023},"citing_paper":{"arxiv_id":"2412.14132","last_updated":"2024-12-18T18:21:41Z","snapshot_observed_at":"2026-08-15T04:14:05.624395Z","submitted_at":"2024-12-18T18:21:41Z","title":"jinns: a JAX Library for Physics-Informed Neural Networks","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-11T12:29:34.507065Z"},"links":{"citing_paper":"/paper/2412.14132"},"observation_digest":"sha256:23e28e978450a1a15f3a080be8c014bff18a3f29b095255a6e4112d014bf6b0d","observation_id":"db17b71d-6644-4eee-8465-86da45089a36","resolution":{"observed_at":"2026-08-11T12:29:35.161787Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T12:29:35.104913Z","title":"Hyperpinn: Learning parameterized differential equations with physics-informed hypernetworks","venue":null,"work_id":"d3b5730b-90bb-4a03-bf06-8dac29fb7480","year":2021},"citing_paper":{"arxiv_id":"2412.14132","last_updated":"2024-12-18T18:21:41Z","snapshot_observed_at":"2026-08-15T04:14:05.624395Z","submitted_at":"2024-12-18T18:21:41Z","title":"jinns: a JAX Library for Physics-Informed Neural Networks","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-11T12:29:34.518150Z"},"links":{"citing_paper":"/paper/2412.14132"},"observation_digest":"sha256:5ef9b2213bef2fdfd5cf2836bf6eafd26898a4ede1b4654b25d9e2961c32f6cd","observation_id":"727303ac-b875-47df-8b42-e19c901c1b59","resolution":{"observed_at":"2026-08-11T12:29:35.121766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.08827","last_updated":"2023-10-05T06:33:52Z","snapshot_observed_at":"2026-08-14T23:19:42.878527Z","submitted_at":"2023-06-15T02:49:05Z","title":"PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.08827","snapshot_observed_at":"2026-08-11T12:29:34.537720Z","title":"Pinnacle: A comprehensive benchmark of physics-informed neural networks for solving pdes","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.14132","last_updated":"2024-12-18T18:21:41Z","snapshot_observed_at":"2026-08-15T04:14:05.624395Z","submitted_at":"2024-12-18T18:21:41Z","title":"jinns: a JAX Library for Physics-Informed Neural Networks","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-11T12:29:34.537720Z"},"links":{"cited_paper":"/paper/2306.08827","citing_paper":"/paper/2412.14132"},"observation_digest":"sha256:2bc306e1cd97c49f6de68be2e2e582959722ccfcdb04f8e407fc8824c73a7f0e","observation_id":"ab36142b-d56f-4140-83cd-cc222e6f07c9","resolution":{"observed_at":"2026-08-11T12:29:34.537720Z","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":"2026-08-11T12:29:34.559410Z","title":"Physics-informed machine learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.14132","last_updated":"2024-12-18T18:21:41Z","snapshot_observed_at":"2026-08-15T04:14:05.624395Z","submitted_at":"2024-12-18T18:21:41Z","title":"jinns: a JAX Library for Physics-Informed Neural Networks","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-11T12:29:34.559410Z"},"links":{"citing_paper":"/paper/2412.14132"},"observation_digest":"sha256:cf25038a489fc4678b98dfc8c68cc583c2bf0bfdc7a668c0301f6944a6e33e47","observation_id":"e28a9427-3cce-4fd4-9ac0-28e363d1df81","resolution":{"observed_at":"2026-08-11T12:29:34.559410Z","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":"2026-08-14T20:53:04.124337Z","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":"2026-08-11T12:29:34.567067Z","title":"Fourier neural operator for parametric partial differential equations","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.14132","last_updated":"2024-12-18T18:21:41Z","snapshot_observed_at":"2026-08-15T04:14:05.624395Z","submitted_at":"2024-12-18T18:21:41Z","title":"jinns: a JAX Library for Physics-Informed Neural Networks","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-11T12:29:34.567067Z"},"links":{"cited_paper":"/paper/2010.08895","citing_paper":"/paper/2412.14132"},"observation_digest":"sha256:30d507dda2ec59ab29646a7b6d039fb738348e03027af869075b1104f5b661e4","observation_id":"aed13a26-bcd4-4d12-a4c3-f9fc79887b5c","resolution":{"observed_at":"2026-08-11T12:29:34.567067Z","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":"2026-08-11T12:29:34.577847Z","title":"DeepXDE : A deep learning library for solving differential equations","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.14132","last_updated":"2024-12-18T18:21:41Z","snapshot_observed_at":"2026-08-15T04:14:05.624395Z","submitted_at":"2024-12-18T18:21:41Z","title":"jinns: a JAX Library for Physics-Informed Neural Networks","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-11T12:29:34.577847Z"},"links":{"citing_paper":"/paper/2412.14132"},"observation_digest":"sha256:22dbf6e43e7dcdd378059d47c861df05bd48295b06be70d1190e47bc5e2bd7ca","observation_id":"3c21fb90-6c67-45dd-943a-8f87a025f0cc","resolution":{"observed_at":"2026-08-11T12:29:34.577847Z","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":"2026-08-11T12:29:35.019219Z","title":"Nvidia modulus, 2023","venue":null,"work_id":"42c63105-e5e5-4ef4-acef-a9cc8924822b","year":2023},"citing_paper":{"arxiv_id":"2412.14132","last_updated":"2024-12-18T18:21:41Z","snapshot_observed_at":"2026-08-15T04:14:05.624395Z","submitted_at":"2024-12-18T18:21:41Z","title":"jinns: a JAX Library for Physics-Informed Neural Networks","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-11T12:29:34.592826Z"},"links":{"citing_paper":"/paper/2412.14132"},"observation_digest":"sha256:ae23c3e493ba15e92ddcef698e2769e2ff4ffbe3ce8301789ade4de5147e2b12","observation_id":"6622845c-15cc-4eb6-bea4-4bfad56d463a","resolution":{"observed_at":"2026-08-11T12:29:35.031113Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T12:29:34.976466Z","title":"Idrlnet: A physics-informed neural network library","venue":null,"work_id":"d0f04252-5908-4050-8466-102594c4ae17","year":2021},"citing_paper":{"arxiv_id":"2412.14132","last_updated":"2024-12-18T18:21:41Z","snapshot_observed_at":"2026-08-15T04:14:05.624395Z","submitted_at":"2024-12-18T18:21:41Z","title":"jinns: a JAX Library for Physics-Informed Neural Networks","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-11T12:29:34.628892Z"},"links":{"citing_paper":"/paper/2412.14132"},"observation_digest":"sha256:637a064a97eb04e8995db354b252531416cae6931ebc975932be9227d5d6a425","observation_id":"868b46ed-bc59-4719-83f9-31b2de670b07","resolution":{"observed_at":"2026-08-11T12:29:34.994278Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T12:29:34.640164Z","title":"Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.14132","last_updated":"2024-12-18T18:21:41Z","snapshot_observed_at":"2026-08-15T04:14:05.624395Z","submitted_at":"2024-12-18T18:21:41Z","title":"jinns: a JAX Library for Physics-Informed Neural Networks","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-11T12:29:34.640164Z"},"links":{"citing_paper":"/paper/2412.14132"},"observation_digest":"sha256:a0a8c23a3232c6fe11844fe823f6d57a68e8f9793005d79a22b00dd40e5ef3ff","observation_id":"3b5b7bd9-04d6-4882-bcad-bf82d6e8b878","resolution":{"observed_at":"2026-08-11T12:29:34.640164Z","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":"2026-08-11T12:29:34.913484Z","title":"Spatio-temporal ecological models via physics-informed neural networks for studying chronic wasting disease","venue":null,"work_id":"9a31773b-9536-46c9-a263-ad27934099fc","year":2024},"citing_paper":{"arxiv_id":"2412.14132","last_updated":"2024-12-18T18:21:41Z","snapshot_observed_at":"2026-08-15T04:14:05.624395Z","submitted_at":"2024-12-18T18:21:41Z","title":"jinns: a JAX Library for Physics-Informed Neural Networks","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-11T12:29:34.656478Z"},"links":{"citing_paper":"/paper/2412.14132"},"observation_digest":"sha256:52b42654cb0cb04569bbb2c7f24bc965f1a5f0afedfabcd432d1dbe2439d71d1","observation_id":"99f0fe0c-afa7-402b-b97a-689fcc3efdd1","resolution":{"observed_at":"2026-08-11T12:29:34.920595Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T12:29:34.889121Z","title":"Pdebench: An extensive benchmark for scientific machine learning","venue":null,"work_id":"7e39439e-8141-489e-bebd-2dd8022c7379","year":2022},"citing_paper":{"arxiv_id":"2412.14132","last_updated":"2024-12-18T18:21:41Z","snapshot_observed_at":"2026-08-15T04:14:05.624395Z","submitted_at":"2024-12-18T18:21:41Z","title":"jinns: a JAX Library for Physics-Informed Neural Networks","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-11T12:29:34.667868Z"},"links":{"citing_paper":"/paper/2412.14132"},"observation_digest":"sha256:ab0e108c545c2b3379de7f0296871f2f229c82e0696b237ba74d6032c19d099d","observation_id":"6ac42f95-607d-49e8-b60c-d8744d3bad99","resolution":{"observed_at":"2026-08-11T12:29:34.899770Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.09443","last_updated":"2021-07-19T12:38:31Z","snapshot_observed_at":"2026-08-11T15:42:58.935231Z","submitted_at":"2021-07-19T12:38:31Z","title":"NeuralPDE: Automating Physics-Informed Neural Networks (PINNs) with Error Approximations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.09443","snapshot_observed_at":"2026-08-11T12:29:34.686969Z","title":"Neuralpde: Automating physics-informed neural networks (pinns) with error approximations, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.14132","last_updated":"2024-12-18T18:21:41Z","snapshot_observed_at":"2026-08-15T04:14:05.624395Z","submitted_at":"2024-12-18T18:21:41Z","title":"jinns: a JAX Library for Physics-Informed Neural Networks","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-11T12:29:34.686969Z"},"links":{"cited_paper":"/paper/2107.09443","citing_paper":"/paper/2412.14132"},"observation_digest":"sha256:9e395a06fb4602629623ce498fa43f00aff54811fa6a48d0f9e18cf2938c5e5b","observation_id":"dac723bf-275a-4ed0-b115-64ba2f3c389c","resolution":{"observed_at":"2026-08-11T12:29:34.686969Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.14132","last_updated":"2024-12-18T18:21:41Z","latest_version":1,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-15T04:14:05.624395Z","submitted_at":"2024-12-18T18:21:41Z","title":"jinns: a JAX Library for Physics-Informed Neural Networks"},"reference_resolution":{"displayed":17,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":1,"verified_fuzzy":10},"total_outbound_references":17},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2412.14132."}