{"as_of":"2026-08-23T15:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:79dc0cf76718b2df4d110b22df26227c354d367d74a08659a5481c7e5e70e1e7","coverage":[{"denominator":58,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":58,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-14T04:40:04.147707Z","state":"measured"},{"denominator":58,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":58,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+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/2607.11576/citation-record","integrity":"/paper/2607.11576/integrity","json":"/paper/2607.11576/citation-record.json","paper":"/paper/2607.11576"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:40:04.147707Z","title":"Stankovic, B","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:b6d81c55ec46dbc8848c95f511338d6118ffea6bdbec7e57c08f7819242816ce","observation_id":"a34aa595-2055-43d1-bffe-1a08dc7d697d","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"Miyazaki, K","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:d8cdaff3ecd5ec2ac1d143e3bc6980555b7701d62409dc898c9acfec4fd97e44","observation_id":"46b42d65-e2e1-4e2e-8853-3790a8a079bd","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"Sughimoto, Y","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:d285d0c1c14ce79c8f047a0e220100bcce5d8eb3f97ef0c257598b565f5fbe42","observation_id":"34c77081-572c-4809-8a0c-661f589c688d","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"Markl, A","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:3898b519c3a9cc6f9037db25faf34cfb034082e95d71827854b8a2a4d2eca19b","observation_id":"d1211efc-9ba7-4dbe-b270-50448969ec7b","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"Casas, J","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:0423ea173e8180cc372d8bbbb195ed4783200cbfb3ab8f87ac2106501a57a3c6","observation_id":"5ca93608-4526-479f-94bd-9d229b1285ba","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":null,"venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:5f205152f2701378ec9c3777b57d7ece9e8ddee210aac6d1a8bf6d91045c8916","observation_id":"6e4efac5-a98e-4f83-a2ae-69af57b91c62","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":null,"venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:5a5cee0df09c6bb0b49edffe37512af71a762f6c046f10b0702316077033901f","observation_id":"7b7a4eac-caa9-4ca0-acba-070f1b4dcbf8","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"Zingaro, L","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:0be188a1da17927d36e79b40de3571c5735238174234d950744cbda2ce0a2b6e","observation_id":"6033212c-9709-42ce-8b3b-12eb8d6edc57","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":null,"venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:2db757f9e6e535458df3a61778691cc060edf7ceb5d553e55a0796122fa89d01","observation_id":"7ef5e084-05f9-4942-b7fe-d2e236dbe07b","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:b6973c17a7f64a31b207919320327987bea94b9f697a0e79f478b0a0d827a5d6","observation_id":"dd61d5d1-8fd8-4841-902a-56eeeb37039f","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:7739d0c5a95d4e100e5218b3c5522285d277e30a7c8fd0d1cd2a00386c9f7bd3","observation_id":"175a2180-37ea-4907-9e13-2702498d0b66","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"10.3934/dcdss.2022052","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Zingaro, I","venue":"Discrete and Continuous Dynamical Systems - S","work_id":"9d8b1a39-441d-4e33-a763-c4fe339955cc","year":2022},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:f7dc60b05017bb20f34d882b7677e9d0ebd37da51f3d8ff7401d78e7fed24ee3","observation_id":"8ddf2e5a-cded-41a1-921f-52fd18abf779","resolution":{"observed_at":"2026-07-14T04:40:13.647978Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-07-14T07:49:49.896996+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T07:49:49.896996+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-07-14T04:40:04.147707Z","title":"Futami, T","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:6d2302f53104f78e76dea35001f4eca516532a9ba139087f15075ac78fbd60c7","observation_id":"fbbadd9e-71bb-4b2d-b99e-eefa4af37160","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":null,"venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:70b4c95f1af9463102d681d1b024fef78c439154e6467247c49a7f649ff012f8","observation_id":"34b095f4-7187-4e2e-8ea6-68280aadd948","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"Quarteroni, Numerical Models for Differential Problems, volume 16 ofModeling, Simulation and Applications, 3rd ed., Springer International Publishing, Cham, Switzerland, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:123eb31fd78ee373987cf57e114bb020f9f19b7539a07915b364c5176f44085c","observation_id":"c7869c23-62ee-4636-9db6-7e22073442d2","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"10.3390/jpm12091502","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Totorean, I.-C","venue":"Journal of Personalized Medicine","work_id":"b90bd51f-70bf-4ba3-91e5-10298f4b9392","year":2022},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:b97ed2fecb312f591b3e636a86173db454a7567a3eaea69d1abca2f562e639c2","observation_id":"4347f8d3-6855-4747-ba8d-47b9fbc8d713","resolution":{"observed_at":"2026-07-14T04:40:13.652643Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-07-14T07:49:50.237226+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T07:49:50.237226+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-07-14T04:40:04.147707Z","title":"Goodfellow, Y","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:89ae647cf2086e514c25c8e2a4274edfb7dd400d3842aebae238bb139b839f45","observation_id":"27a48649-c9c6-4129-bf5b-45cc87ce95d3","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"10.3934/mine.2023032","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tassi, A","venue":"Mathematics in Engineering","work_id":"27cea811-5a1f-46e2-8d6e-f85a5a4f394c","year":2023},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:9230569acae257afd90c9e590269a46e02039aff58053f1dbfc31c298f09ba63","observation_id":"b70ae520-52ca-4469-a65a-2a6518f70279","resolution":{"observed_at":"2026-07-14T04:40:13.679561Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-07-14T07:49:50.547557+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T07:49:50.547557+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-07-14T04:40:04.147707Z","title":"Quarteroni, P","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:ad05994ed4556940df25a8d68ecc20131b72f776d72dab254ef4293c0a6456d1","observation_id":"777c8f59-0ce1-46d3-9304-82ca02e5a7d1","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:813380d7c7adf1de042b0b3568ed1b876473a2e4ed5dad98bc915ae4a6b82e99","observation_id":"df5e2c1e-e68d-4361-8acd-73f4319b8d90","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.10561","last_updated":"2017-11-28T21:21:59Z","snapshot_observed_at":"2026-08-15T08:52:31.204350Z","submitted_at":"2017-11-28T21:21:59Z","title":"Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.10561","snapshot_observed_at":"2026-07-14T04:40:04.147707Z","title":"Raissi, P","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"cited_paper":"/paper/1711.10561","citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:6e828666c23e8467eff8a6051416125d5c17ef5b59d52482aef6f1ddbc7acab1","observation_id":"cbfd77d9-87bc-400d-92ae-3c0e44c88f9f","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"10.1007/s13239-024-00762-x","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Cardiovascular Engineering and Technology","work_id":"453e171f-3410-450e-bd69-01ab191a55a1","year":2025},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:33fe5eb4e00933ab779fe9117ad85376efa7b6e881df3e58d02d66ccb1fc0c61","observation_id":"c783dd2c-6e1a-4a92-9c11-a161f34e7474","resolution":{"observed_at":"2026-07-14T04:40:13.637307Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-07-14T07:49:51.543344+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T07:49:51.543344+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-07-14T04:40:04.147707Z","title":"Zhang, M","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:d671edb8680e74b74162c4aac29528b96f01b8716c94e583e13840dff3e1ac6d","observation_id":"8084acf4-ab90-4a5c-a146-c86f11eea074","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"Ferdian, D","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:30e9d76b35ef3dd13e54f0b59fc17c993f801833affad8dbd0593bc57fb6772c","observation_id":"04de8a2d-df49-4aa7-b751-5af69e6055d3","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2021.308633","doi":"10.1109/tmi.2021.3086331","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Zhang, S","venue":"IEEE Transactions on Medical Imaging","work_id":"5b93e88d-8397-4088-955b-0759c170318a","year":2021},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:c5174d5c094fe77488547c55572909a258e7070c9674a05aeb954d67e91ef1bb","observation_id":"c49e6fcd-afcd-4d77-baf5-0537a37f8d33","resolution":{"observed_at":"2026-07-14T04:40:13.659520Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-07-14T07:49:51.772197+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T07:49:51.772197+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-07-14T04:40:04.147707Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:88bb09d13151011e66dfaa7c6048354314018849c539fa8cb4da3ceebad4d57f","observation_id":"0a140c69-abe2-4926-b260-c3cfacfc4023","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"Kissas, Y","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:65902a193628c5058b7297f7ec9c1740e0fcc7e5acdbf7088686bda52b88a596","observation_id":"f3d806cd-1199-460a-9eb5-25f30ad48517","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"Sarabian, H","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:894cc6b6f689700762f348e7f5e8aa89614bb5854218c8fbf84d2a58cade3c2b","observation_id":"7ddd8e45-c20f-45cc-9390-57a8afac62ce","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"URL:https://www.cambridge.org/core/ product/identifier/S002211202100135X/type/journal_article","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:10af4dbe6a0e0b36f5d1253303aa356fd6976835e5799a4f766eb8a0ec9fa8a0","observation_id":"3c51f36a-e249-49f1-a44a-4a89a2b0ab74","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"Hub, Benchmark dataset for validating computational fluid dynamic (CFD) simulation of blood flow through FDA nozzle and FDA blood pump — NCI Hub, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:256ed692a79ebbb53b901603aba6f6f120870f2752162e8ace6d018a39388110","observation_id":"a6fd554c-f89f-4be9-81b7-d46c8d7c7cb8","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":null,"venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:9cd51f99456d3ceadcb440ebe4c6db5af424c2474e40ee6bf2fe8845b17eda3d","observation_id":"160956e6-ad96-453d-8f97-6c95cd279d17","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"Hariharan, M","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:d02604e36920aede19110b41e89a004b214ca2ec440d09ef53e0f9a1a2b9b533","observation_id":"f6d4f7aa-aa6b-4f0b-89fb-b91919d42e61","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"10.17917/c78g69","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Hariharan, R","venue":"National Cancer Institute","work_id":"dd4c1629-dca9-49c9-915b-b6ee2253e8c4","year":2017},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:69d0e73c0d9c155825c46fbb545d7807d8da8c0d1a62b16a435db3b419a33021","observation_id":"0c41e95d-193a-42b2-95d8-89274440ce97","resolution":{"observed_at":"2026-07-14T04:40:13.673907Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-07-14T07:49:52.240737+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T07:49:52.240737+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-07-14T04:40:04.147707Z","title":"Quarteroni, L","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:6b5ee960058fc847766cb8613a98626d80d7f5c3d03d2e877c6f27200eb452e8","observation_id":"560fb2cb-6234-495e-b4bf-1555773c7749","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"Bischof, M","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:be54765c81b40cc4ed86f10c39fdc65049483f8c1df43b329a40b7f12ae558a4","observation_id":"dc8eedbe-b881-40e8-96b1-e30a21cf6496","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"Kissas, E","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:c6bb3f5a7fba328ef4f89823dfe29d17fae0871ff01f5d2439772959857b1e22","observation_id":"28eecb0f-a563-4cd0-a7d3-9b5660db625c","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"Chollet, Deep Learning with Python, Simon and Schuster, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:7cbbbce80f474a9a5960987d3da74d5414c638d6c7840ec8b2c384907c6e4f5a","observation_id":"c1d5ccb9-7d39-4044-9834-fe2800833283","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.07805","last_updated":"2022-04-16T14:14:13Z","snapshot_observed_at":"2026-08-16T17:06:34.399926Z","submitted_at":"2022-04-16T14:14:13Z","title":"Universal Solution Manifold Networks (USM-Nets): non-intrusive mesh-free surrogate models for problems in variable domains","version":1},"cited_work":{"arxiv_id":"2204.07805","doi":"10.48550/arxiv.2204.07805","metadata_source":"pith","pith_arxiv_id":"2204.07805","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"Universal Solution Manifold Networks (USM-Nets): non-intrusive mesh-free surrogate models for problems in variable domains","venue":"math.NA","work_id":"2ece7f00-0583-460a-8320-0dae9bd093e9","year":2022},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"cited_paper":"/paper/2204.07805","citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:cffd762737627241075c020a30b65b927d149714843eabe7bb4f1c1a718a4740","observation_id":"1050ef70-1028-4bba-95f2-de485c396442","resolution":{"observed_at":"2026-07-14T04:40:13.664421Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-07-14T07:49:52.785199+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T07:49:52.785199+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-17T19:26:44.032537Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-07-14T04:40:04.147707Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:d6975071687e56ce5a96782fdc50908660c0da7e3e692bc00f6c6ba87003ed8f","observation_id":"bc318eca-d5a5-4470-a4f5-3747fea785d5","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":null,"venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:25aaf278bd685ea8ef0a3a7c8ee5652742c283bba90513fe638414a1cafac562","observation_id":"2df07ecf-e00d-4f89-97fd-cd9eafd90d69","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:7c489a35e654987e44ac11efbd3ea15069b8208a4db968e29cbf79be320d54b1","observation_id":"382fcfea-7357-420f-bb03-49801668039f","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:2e1d874aab05fe4ac9b7d62c210d27f0e226e22db20e563c6d46b3b34fd288de","observation_id":"becad6e1-64fc-4eed-932a-51408670ccb9","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"Arndt, W","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:eab72303628eec5df62566edb5d2de85b6f606f6fb371ee510052ece75839862","observation_id":"17c5b4e6-6f76-4645-b467-1ab3b49645f3","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"10.1515/jnma-2024-0137/html","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:40:13.670527Z","title":null,"venue":null,"work_id":"a0bb3115-1c7c-4928-9c23-27a81c6d631f","year":2024},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:3e3dda7add13c1bf3df95f93f4c364f360af23a4462df024001b0fea7216660e","observation_id":"88cbc2ae-7457-40dd-afcc-eb952009104e","resolution":{"observed_at":"2026-07-14T04:40:13.671964Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-07-14T07:49:53.652675+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T07:49:53.652675+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-07-14T04:40:04.147707Z","title":"Forti, L","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:0a8a8cd8106cad9150779509da8284f403ccce4207f7574e9b4ef98669ae67ea","observation_id":"e724ce23-3dd5-4665-9b9a-92e886d7eeb0","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"URL:https://mox.polimi.it/research-areas/hpcmox/ hardware/, [Accessed 13-Aug-2022]","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:6bb76d36dd88392ae5ef3ef06a6ab1346274dafb986d23c62da5732c03994396","observation_id":"3d4a4b94-6167-4414-8d47-16489d74d3d0","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"10.2514/6.2023-1803","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"AIAA SCITECH 2023 Forum","work_id":"8d993ac0-95cb-4c4f-98fc-a1624f123470","year":2023},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:503d0c1db88adeda739bc5023f2cf9326b99781b9a82c8f898431aa2e7af44fb","observation_id":"c5abc3d5-455e-40aa-beb4-93a8825c82ce","resolution":{"observed_at":"2026-07-14T04:40:13.651595Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-07-14T07:49:53.896546+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T07:49:53.896546+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-07-14T04:40:04.147707Z","title":"Géron, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems, O’Reilly Media, Inc., 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:224f291ca2f64d46fdca8d08750fa0f038e45a297da3bf16dc2a1fb4f6ef4395","observation_id":"9ea99b9b-7c6f-4190-8589-a3c3c7335e3f","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"Ioffe, C","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:63d894894dce9effecb1d68028bfdbc41a1ef8f6af001f3e18d1abfb923c57e5","observation_id":"69b9062f-4ef1-42de-a9b8-9cbc6a3c68d8","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"Glorot, Y","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:dbb1dab8b514f34d08d6a9149708df92216f3cc1cdc951bd1498e18f98a005b3","observation_id":"9c147a4a-d82c-4228-88df-9722e15544b1","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"Raschka, Python Machine Learning, Packt publishing ltd, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:5fc39596d94d0451eb41154b220295db36f73321600cabc10ec36f3ef0a0ccbd","observation_id":"56280755-3876-4e95-adda-c2742036686c","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"Abadi, P","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:849f693a6ef9305381da3d42c8abb3aa2f949ff9e77758a9c76355136d641656","observation_id":"a9e512f3-af10-4a47-9fcb-829c334432f2","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"Chollet, et al., Keras,https://keras.io, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:cd51460770093d82626255854647275ad6fd67b9650d98164c3c2616e8cd18f2","observation_id":"db932fd1-9b90-4965-9c74-a4de68e3a30e","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"Ahrens, B","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:28d6c865fba30ca8a7ec06531c1a873f0f491d63df3ac04a64f67e1ff447b37d","observation_id":"7af35a90-38cf-4b4d-81cc-a060ca1dcec7","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:1fdc9d5c1074d7de94472e1b90e0693732110a430758f03682aa2fa8ab9a8000","observation_id":"d6ef361a-337a-4494-84a8-8df6979c81d7","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"Weiss, V","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:1be6176cfbbf46c71cc14a8b166dc0cfe89f005c6ec6866f78de122bd63bce32","observation_id":"0b0a1ae0-3f7c-4143-b138-3c62a1adc368","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:62867cc4fd9b80a2e6b2ecaacb4077e2edcf6dd505bd16bdfb482957be24ab0d","observation_id":"494bb9bf-83d5-443b-853f-976f82912dac","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","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":"2026-07-14T04:40:04.147707Z","title":"Henry, J","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-07-14T04:40:04.147707Z"},"links":{"citing_paper":"/paper/2607.11576"},"observation_digest":"sha256:81a117988656e2ba7c60998d21df6152a05103a934adcc2d258c047be4b01f91","observation_id":"5b01aad8-c686-4842-9e13-f070c61cfe7d","resolution":{"observed_at":"2026-07-14T04:40:04.147707Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.11576","last_updated":"2026-07-13T13:57:46Z","latest_version":1,"primary_category":"math.NA","snapshot_observed_at":"2026-08-18T02:11:29.835563Z","submitted_at":"2026-07-13T13:57:46Z","title":"Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics"},"reference_resolution":{"displayed":58,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":49,"verified_exact":8,"verified_fuzzy":0},"total_outbound_references":58},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 0 inbound Pith citation observations for arXiv:2607.11576."}