{"as_of":"2026-08-14T20:58:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a9e1fba4734d4f8c1147ba601d332737e1f85d5c83c74d01c64b38c38cd5f697","coverage":[{"denominator":59,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":59,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T14:59:45.023603Z","state":"measured"},{"denominator":59,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":59,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2501.14870/citation-record","integrity":"/paper/2501.14870/integrity","json":"/paper/2501.14870/citation-record.json","paper":"/paper/2501.14870"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T14:59:46.830303Z","title":"Fundamentals of Hydro- and Aeromechanics","venue":null,"work_id":"cddc4b4b-16cd-4614-83e9-1130ffe408e8","year":1957},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.759405Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:d73acf6fd291034619774a719b071d2d0746fd5e48d533718f71db285752fd3a","observation_id":"d8c7578b-34fa-4dd9-a21d-ee7d39f64976","resolution":{"observed_at":"2026-08-10T14:59:46.834293Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T14:59:46.816480Z","title":"An Introduction to Theoretical and Computational Aerodynamics","venue":null,"work_id":"147aff34-d51f-419e-a0d7-287063306a04","year":1984},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.764613Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:1786aaf0f946741510ff94c66e395d8b154cd5ea6e21d0576f6ed6de4680d268","observation_id":"5687713d-ea9f-4a91-93d7-318efb3c0644","resolution":{"observed_at":"2026-08-10T14:59:46.821282Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T14:59:46.801971Z","title":"Geuzaine and J","venue":null,"work_id":"5e7428c1-3dc3-4630-adbb-16f0ac1bab95","year":2009},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.768597Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:d68b94c89206d872b783b763d8d111633eba47a1e616672444210a672a3205d5","observation_id":"ae86e08d-5585-48d3-a74b-bc83bf70cf9f","resolution":{"observed_at":"2026-08-10T14:59:46.806533Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T14:59:46.787776Z","title":"Fast multipole boundary element method: theory and applications in engineering","venue":null,"work_id":"83627330-3481-463d-a34f-23c799cecea7","year":2009},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.772913Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:fc7b7ebeee12e6e1392a311571ca8ad6cdc044b97f1fdae9c1deaf04fe578a3e","observation_id":"46820642-cd72-41c8-baad-e86422d8aa29","resolution":{"observed_at":"2026-08-10T14:59:46.792504Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T14:59:46.773555Z","title":"Abbott and Albert E","venue":null,"work_id":"aa49ee42-9a9b-4bbd-aa4e-fa29bc73a2f0","year":2010},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.777123Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:27f5bfdb34b1f1e79628791b9da1d270c2ab819ded175e9217aa25a976b6f5f7","observation_id":"e2f8d1c5-0fe2-4620-8dd5-b9141a3ee6c5","resolution":{"observed_at":"2026-08-10T14:59:46.778099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T14:59:46.759900Z","title":"Fundamentals of Aerodynamics, Fifth Edition","venue":null,"work_id":"f7d6d572-c3b4-44b8-b6b4-b0b381d3cc69","year":2011},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.781274Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:7427ae20965a4d21f3b2ba6272cf7dad8057273bd6f521f413bba8580c64697d","observation_id":"003f4536-7b1c-4f24-be23-c2b1c2dd1e87","resolution":{"observed_at":"2026-08-10T14:59:46.764411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T14:59:46.745567Z","title":"TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems","venue":null,"work_id":"a65cd029-5c21-41b8-98dd-24ae717e7588","year":2015},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.785682Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:795fe13f156161438e9ed32aa5d3c9e661a6b97b35b7cb4423bca1d07f8467a0","observation_id":"986d28bb-df4e-4ccd-8aef-06b3a0a6b646","resolution":{"observed_at":"2026-08-10T14:59:46.750369Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T14:59:44.789850Z","title":"Convolutional Neural Networks for Steady Flow Approximation","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.789850Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:67f3af38ec4128b54e7b6f3fa58322b3d7b83982e626dd891ead27de0a25abdb","observation_id":"eb57a835-7c85-451a-872d-85cb9db7a382","resolution":{"observed_at":"2026-08-10T14:59:44.789850Z","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":"2026-08-10T14:59:44.793902Z","title":"A Review of Variational Multiscale Methods for the Simulation of Turbulent Incompressible Flows","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.793902Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:5fab6adc6783c91feb1f39dbaf76640662dd9e285964dda495f2a31e5bde32a9","observation_id":"c864e0b7-fe7d-4c09-b459-72adf10a5cb0","resolution":{"observed_at":"2026-08-10T14:59:44.793902Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-10T14:59:46.731922Z","title":"Sobolev Training for Neural Networks","venue":null,"work_id":"038f5b3d-d00b-4d05-b52a-f06db9df97ed","year":2017},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.798591Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:9e0f6695a8d8230f7ffbfa4ab6eb64d52c7bd5e026b1df3ec74c1889be5a796e","observation_id":"45bf6fb7-9aad-4380-a56a-d363eab518dc","resolution":{"observed_at":"2026-08-10T14:59:46.735863Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T14:59:46.719204Z","title":"Spiral: A General Framework For Parameter Sensitivity Analysis","venue":null,"work_id":"7420b082-e294-4a8f-bf28-db0e838f22e5","year":null},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.804039Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:fbfdc70ea321383519889d8950edbd8ecf693ba0c9bb9afb7683038758bae61f","observation_id":"594033fa-bf27-4c24-be0c-f14c56afcb6e","resolution":{"observed_at":"2026-08-10T14:59:46.723184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T14:59:46.706213Z","title":"Graph Element Networks: adaptive, structured computation and memory","venue":null,"work_id":"3f3ab5fd-c667-4271-acb5-5d3e01040626","year":2019},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.814851Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:708ad3762f2eb67cee6586337c4d1188aee409573e69c299a851556848673877","observation_id":"73ecd2bd-862e-42e6-90b8-14b141e0eee8","resolution":{"observed_at":"2026-08-10T14:59:46.710573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s00158-018-2110-4","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T14:59:45.403074Z","title":"Concurrent shape and topology optimization for steady conjugate heat transfer","venue":null,"work_id":"bc9f2454-8e04-4813-8f83-be86d56fc58c","year":2019},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.819283Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:820e8d4ec938a569e3f3ab3f7a8b2ba489155e1e93418fdc9e0fc39181c0d9d0","observation_id":"ceeb21e1-c033-43fc-b511-bb70de4044ef","resolution":{"observed_at":"2026-08-10T14:59:45.407035Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T14:59:44.823832Z","title":"Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial dif- ferential equations","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.823832Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:8bc75b161f5a30a016cfbbdd3687e057933e9d449dadbb3cc60d38a75a898835","observation_id":"624049d6-8a26-498f-97f4-52fcd76f266c","resolution":{"observed_at":"2026-08-10T14:59:44.823832Z","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":"2026-08-10T14:59:46.693032Z","title":"Optimize TensorFlow & Keras models with L-BFGS from TensorFlow Prob- ability","venue":null,"work_id":"87077402-b47d-4c23-ae1e-50416888f320","year":2019},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.828430Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:efbc639781389815da9dfca0847d75613d093ea62c38d0acd5141df88e04e5a5","observation_id":"543b66a4-51dd-45d4-955e-e9e229b4f96f","resolution":{"observed_at":"2026-08-10T14:59:46.697513Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T14:59:46.677745Z","title":"Combining Differen- tiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction","venue":null,"work_id":"cc3a2b50-0deb-4ccf-aea7-a35350c1e3d9","year":2020},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.833856Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:a758c7d31e86a9ff6985e8b861f3b7d14fe2ae782b5f786f2915813f8ea8bac5","observation_id":"d856af40-210f-4d3c-99e6-372f4e70efba","resolution":{"observed_at":"2026-08-10T14:59:46.682558Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T14:59:46.661930Z","title":"Machine Learning for Fluid Mechanics","venue":null,"work_id":"deea9338-c5bb-42fa-93d7-fa67ddfff150","year":2020},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.837981Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:154a72fc1678085d83afb0170d980295c494dd42088fad2bff1baddf69410411","observation_id":"1a9f554c-48f2-4bbb-8e8e-3a249a58194d","resolution":{"observed_at":"2026-08-10T14:59:46.667187Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.2514/6.2020-1409","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T14:59:45.378904Z","title":"Enhancement of Low Fidelity Fluid Simulations using Machine Learn- ing","venue":null,"work_id":"25b5bfee-ced6-4223-8bdf-98040b40980b","year":2020},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.842213Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:a852401aec2694a0ae3e9f70b1f9713676ffc42522c3130f5336dd132a78600b","observation_id":"77b90e64-2ee4-4056-af92-f135c0f24f88","resolution":{"observed_at":"2026-08-10T14:59:45.383645Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T14:59:44.846499Z","title":"Conservative physics- informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.846499Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:6e1f469b25407d91ac2f1f8b1d9df45bdc509991c2610a1b2da50d29d3bf20cf","observation_id":"5d66942c-21e3-4f1b-be27-7ffd002898e1","resolution":{"observed_at":"2026-08-10T14:59:44.846499Z","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":"2026-08-10T14:59:44.851002Z","title":"Fourier Neural Operator for Parametric Partial Differential Equations","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.851002Z"},"links":{"cited_paper":"/paper/2010.08895","citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:e3dd528f603e95f432b6d155aa2b52cf0c5f023b3c90cf33e63b6c89aa839816","observation_id":"af7f8e17-75b1-4244-9888-d0c7f6c3e5d9","resolution":{"observed_at":"2026-08-10T14:59:44.851002Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.03485","last_updated":"2020-03-07T01:56:20Z","snapshot_observed_at":"2026-08-03T02:26:02.483819Z","submitted_at":"2020-03-07T01:56:20Z","title":"Neural Operator: Graph Kernel Network for Partial Differential Equations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.03485","snapshot_observed_at":"2026-08-10T14:59:44.855680Z","title":"Neural Operator: Graph Kernel Network for Partial Differential Equations","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.855680Z"},"links":{"cited_paper":"/paper/2003.03485","citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:6a90f1c8d1021d0d09c8795332b514f415de1535414c66a97662aa409820b89b","observation_id":"b91d5ed5-de7e-4044-89f8-415f5f7ef428","resolution":{"observed_at":"2026-08-10T14:59:44.855680Z","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/s00158-020-02554-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T14:59:45.363766Z","title":"Concurrent shape and topology optimization for unsteady conjugate heat transfer","venue":null,"work_id":"e00d6c85-2510-4100-911f-5b26d5dc349e","year":2020},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.860593Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:ffc329c3ac291f16aaffb1880ed1e5217d44eb10a534628574b2281677df048f","observation_id":"ba3fdf00-4c05-46d5-87ed-45b7c1c8299f","resolution":{"observed_at":"2026-08-10T14:59:45.369032Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T14:59:46.647677Z","title":"Physics-informed neural net- works for high-speed flows","venue":null,"work_id":"490c6d7e-1e9f-4fea-9e0b-37c039341082","year":2020},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.865322Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:b36a0c97646719d0065a698b6013d6e36970091d4dd495f410b43d341bc48885","observation_id":"fb43a7b1-ab5b-4752-a681-3ae8cbc54df5","resolution":{"observed_at":"2026-08-10T14:59:46.652608Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2020.10477","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T14:59:46.253326Z","title":"A turbulent eddy-viscosity surrogate modeling framework for Reynolds- Averaged Navier-Stokes simulations","venue":null,"work_id":"5b4fe154-5a90-4e5d-9f98-f35bafa022b5","year":2020},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.870557Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:36c532adec2f0ddb145c7f7eec2af77c67130735d65cc1fc7e100d714e36c3e0","observation_id":"49ea6f72-8120-4934-a183-ff03a229d17c","resolution":{"observed_at":"2026-08-10T14:59:46.260385Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T14:59:44.875537Z","title":"CFDNet: A Deep Learning-Based Accelerator for Fluid Simu- lations","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.875537Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:27d3e5f3b69b86e06c70fdca4f4f8ee52270c4f2cee4ecd16d9cb1a513c9a7e9","observation_id":"47fd9128-3e4c-485f-bcd9-c83c59c2a400","resolution":{"observed_at":"2026-08-10T14:59:44.875537Z","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":"2026-08-10T14:59:44.879782Z","title":"Hidden fluid mechanics: Learn- ing velocity and pressure fields from flow visualizations","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.879782Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:0a4bf266d2655ae0fe69fd7a55766d8a14e8cb881cffbb23cc8919d833e348ac","observation_id":"23fc3db9-97fc-4196-9e3e-354d4cffc303","resolution":{"observed_at":"2026-08-10T14:59:44.879782Z","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":"2026-08-10T14:59:46.632450Z","title":"Physics-informed deep learning for incompress- ible laminar flows","venue":null,"work_id":"f517b412-eeb3-4f4a-93fd-2e9ef3cddb53","year":2020},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.883895Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:62f1e983c10547402348ec92f652b7182256863e7b808f07c09f0182cf563b0b","observation_id":"ae77b40e-c010-4f7c-a5d8-c9e84f45211a","resolution":{"observed_at":"2026-08-10T14:59:46.637188Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T14:59:44.888382Z","title":"Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.888382Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:f9433740efa9ab6906c4a1d1716f18269c01592e4cb380e3b8279df176ea6568","observation_id":"502687e5-6986-425e-a8b2-20fc38cf0884","resolution":{"observed_at":"2026-08-10T14:59:44.888382Z","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":"2026-08-10T14:59:44.892529Z","title":"Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.892529Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:cb7697ad813334504641a990ff788ccc104bf1abf41294acef1af210f7ea1c35","observation_id":"ecda687b-42be-4bf2-883e-d9300e6bff71","resolution":{"observed_at":"2026-08-10T14:59:44.892529Z","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":"2026-08-10T14:59:44.896972Z","title":"Physics-informed neural networks (PINNs) for fluid mechanics: a review","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.896972Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:1582238877d0fe9ac79a9b1d5f0292135dc173583e09e8465be3ac61426c6559","observation_id":"638de3dc-848f-4018-846a-07b074d94fa0","resolution":{"observed_at":"2026-08-10T14:59:44.896972Z","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":"2026-08-10T14:59:44.901165Z","title":"Transfer learning for deep neural network-based partial differential equa- tions solving","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.901165Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:5d5effb123b6efd9131576fadae8e29001fb7c2f1f414070d20d9c9ef5b0a94b","observation_id":"e49b7318-11ee-422d-abc2-fae5d792062b","resolution":{"observed_at":"2026-08-10T14:59:44.901165Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.11286","last_updated":"2022-07-05T15:59:38Z","snapshot_observed_at":"2026-08-13T17:48:39.382836Z","submitted_at":"2021-10-21T17:14:58Z","title":"One-Shot Transfer Learning of Physics-Informed Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.11286","snapshot_observed_at":"2026-08-10T14:59:44.905423Z","title":"One-Shot Transfer Learning of Physics-Informed Neural Networks","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.905423Z"},"links":{"cited_paper":"/paper/2110.11286","citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:1740e86003a45008f3daeb757133ff3d300c4fa0c494c419a8816461b1753419","observation_id":"ce1faa16-def8-4de5-9e48-064ca2c150b9","resolution":{"observed_at":"2026-08-10T14:59:44.905423Z","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":"2026-08-10T14:59:46.616635Z","title":"PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular do- main","venue":null,"work_id":"72a3e242-1c69-4f78-8cca-5c70fef98a64","year":2021},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.909291Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:cc86842f61efddbcb10efd39042b1a9c4156e3bdb679790a82be956383b54a3d","observation_id":"2464ad42-00cb-465f-abc7-29b99bb3d10b","resolution":{"observed_at":"2026-08-10T14:59:46.621701Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T14:59:44.912715Z","title":"NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.912715Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:532723beb50b5c61213f7556115508c97a4c54db0ec9bf255d9296101b721020","observation_id":"74b00b47-5639-4000-afc5-6b864bc84e49","resolution":{"observed_at":"2026-08-10T14:59:44.912715Z","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":"2026-08-10T14:59:44.916205Z","title":"Physics-informed machine learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.916205Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:203d78d1a5e8883a2571fc6541aaf81bad180458df325458da258e1b218575ec","observation_id":"4d47c663-dbc1-4d5b-a826-4adab1474dbf","resolution":{"observed_at":"2026-08-10T14:59:44.916205Z","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":"2026-08-10T14:59:44.920345Z","title":"A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.920345Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:6727882fb5e81da56fc31a40a62d5e037c91ade7d065ff5eff63ea01e6a1672a","observation_id":"0d6a5f20-3be1-4907-9a2c-64d5ceb1c935","resolution":{"observed_at":"2026-08-10T14:59:44.920345Z","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":"2026-08-10T14:59:46.601727Z","title":"Characterizing possible failure modes in physics-informed neural networks","venue":null,"work_id":"11bd9b8e-045e-4c78-9739-f7f7178e630b","year":2021},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.924072Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:eb8ad5bedae9577fcb67cf0bce166c815e11936e28186fe1b3f4e1224e04b95a","observation_id":"4f833d90-ddf6-4092-9e1e-75f87efeb5a9","resolution":{"observed_at":"2026-08-10T14:59:46.606836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T14:59:44.928687Z","title":"Learning nonlinear operators via DeepONet based on the universal approxima- tion theorem of operators","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.928687Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:203313ea201ddc5f04d4019a762a53a45a3935d88c4c9ceefef698d0bd3d1766","observation_id":"97ab5c4b-6ba3-4fdd-a204-ad9262df3f99","resolution":{"observed_at":"2026-08-10T14:59:44.928687Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-10T14:59:44.933301Z","title":"SURFNet: Super-Resolution of Turbulent Flows with Transfer Learning using Small Datasets","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.933301Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:c013dd6ff9452f09d27657d1e59e0399974000cffc7e3f54a66f11bbad59ee3e","observation_id":"8bb66792-eb03-4ee7-9775-919edcae5aa5","resolution":{"observed_at":"2026-08-10T14:59:44.933301Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.13361","last_updated":"2023-01-19T22:20:49Z","snapshot_observed_at":"2026-08-13T17:45:57.084849Z","submitted_at":"2021-10-26T02:29:10Z","title":"A Metalearning Approach for Physics-Informed Neural Networks (PINNs): Application to Parameterized PDEs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.13361","snapshot_observed_at":"2026-08-10T14:59:44.938018Z","title":"Physics-Informed Neural Networks (PINNs) for Parameterized PDEs: A Metalearning Approach","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.938018Z"},"links":{"cited_paper":"/paper/2110.13361","citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:ab9c13111cc9909aa99cbf95fec3f83dab0acd67f021d07d1e7d3d1678110c56","observation_id":"fb42c71d-de44-4de6-a65d-9bb9e188f71b","resolution":{"observed_at":"2026-08-10T14:59:44.938018Z","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":"2026-08-10T14:59:44.942735Z","title":"A Comprehensive Survey on Graph Neural Networks","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.942735Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:de3dc2ccff9b40b931b88a20bd3e89c03be97016b07f847fda67efe22453c125","observation_id":"6a4b9abc-bb83-4d91-b86b-c8126e399ea0","resolution":{"observed_at":"2026-08-10T14:59:44.942735Z","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":"2026-08-10T14:59:44.947138Z","title":"Physics and equality constrained artificial neural net- works: Application to forward and inverse problems with multi-fidelity data fusion","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.947138Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:7b891d003855ef9ec97f6af04f625cad9f9328a2bf87d4549a2a678418833199","observation_id":"537876e0-7bed-46bf-8815-d19b90bae138","resolution":{"observed_at":"2026-08-10T14:59:44.947138Z","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":"2026-08-10T14:59:44.951756Z","title":"CAN-PINN: A fast physics-informed neural network based on coupled- automatic-numerical differentiation method","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.951756Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:ae0feb43549e8b57dfe6775c56d62f48f0148528bc4fb4d143e782f61179f4d6","observation_id":"65be49ea-ee8b-495e-9fe9-7c280d53e8a4","resolution":{"observed_at":"2026-08-10T14:59:44.951756Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.14249","last_updated":"2022-07-23T01:32:58Z","snapshot_observed_at":"2026-08-13T15:35:03.620997Z","submitted_at":"2022-05-27T21:54:12Z","title":"Experience report of physics-informed neural networks in fluid simulations: pitfalls and frustration","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.14249","snapshot_observed_at":"2026-08-10T14:59:44.957874Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.957874Z"},"links":{"cited_paper":"/paper/2205.14249","citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:702bc556732decc412e27e2cdf119df7be66887ca781011b3ca30a230ce83200","observation_id":"18e0276a-edc8-4fbf-b812-d192617be8b4","resolution":{"observed_at":"2026-08-10T14:59:44.957874Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.05624","last_updated":"2022-06-07T07:37:30Z","snapshot_observed_at":"2026-08-13T16:59:03.539050Z","submitted_at":"2022-01-14T19:05:44Z","title":"Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.05624","snapshot_observed_at":"2026-08-10T14:59:44.963916Z","title":"Scientific Machine Learning through Physics-Informed Neural Net- works: Where we are and What’s next","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.963916Z"},"links":{"cited_paper":"/paper/2201.05624","citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:b62b42ab9e3ee8663f3587ebd44b560f44cd82c7d3a9bf5e5f7ba0cb1471a4ad","observation_id":"ab852649-ff8d-47bf-ba92-e1f83638e6f4","resolution":{"observed_at":"2026-08-10T14:59:44.963916Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.15402","last_updated":"2024-02-22T17:37:31Z","snapshot_observed_at":"2026-08-13T16:13:26.125204Z","submitted_at":"2022-03-29T09:58:30Z","title":"Physics-informed deep-learning applications to experimental fluid mechanics","version":2},"cited_work":{"arxiv_id":"2203.15402","doi":null,"metadata_source":"pith","pith_arxiv_id":"2203.15402","snapshot_observed_at":"2026-08-10T14:59:45.715870Z","title":"Physics-informed deep-learning applications to experimental fluid mechanics","venue":"physics.flu-dyn","work_id":"2a59ee02-3955-400c-8802-fd0d66958427","year":2022},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.968911Z"},"links":{"cited_paper":"/paper/2203.15402","citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:2fbe32709720891cb65832059a2b3146fcccdab56119d4d21b34c7d71aeb51f5","observation_id":"5332ad0b-4da6-449d-90dd-14d6071d32ab","resolution":{"observed_at":"2026-08-10T14:59:45.720662Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T14:59:46.585631Z","title":"Physics-informed neural networks for solving Reynolds-averaged Navier-Stokes equations","venue":null,"work_id":"fe56257e-1ee1-4cb4-85d8-8af658462fc5","year":2022},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.973732Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:71df79110b7705d6cfdeb1ad4ceb0a621cbaada3e89b7ed074c33e095b6798dd","observation_id":"ef724b47-ee94-428d-b293-69efb5c79213","resolution":{"observed_at":"2026-08-10T14:59:46.591105Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.11821","last_updated":"2022-02-23T23:19:41Z","snapshot_observed_at":"2026-08-13T16:35:06.037682Z","submitted_at":"2022-02-23T23:19:41Z","title":"Physics-informed neural networks for inverse problems in supersonic flows","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.11821","snapshot_observed_at":"2026-08-10T14:59:44.978667Z","title":"Physics-informed neural networks for inverse problems in supersonic flows","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.978667Z"},"links":{"cited_paper":"/paper/2202.11821","citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:63730d72583a2023d0b3dc08468bad062134ef7bc736589db4835d602328cb8d","observation_id":"e71b1be3-913e-4979-8786-effb09f4976e","resolution":{"observed_at":"2026-08-10T14:59:44.978667Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.07141","last_updated":"2022-12-02T01:55:19Z","snapshot_observed_at":"2026-08-13T16:40:44.431083Z","submitted_at":"2022-02-15T02:23:21Z","title":"Machine Learning in Aerodynamic Shape Optimization","version":2},"cited_work":{"arxiv_id":"2202.07141","doi":null,"metadata_source":"pith","pith_arxiv_id":"2202.07141","snapshot_observed_at":"2026-08-10T14:59:45.682604Z","title":"Machine Learning in Aerodynamic Shape Optimization","venue":"cs.LG","work_id":"887486fa-0b52-435e-a773-4e1163659d15","year":2022},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.983446Z"},"links":{"cited_paper":"/paper/2202.07141","citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:905ff5ae0d2d39b0a75227befda9b344de25cc95567a55a1e806984709674114","observation_id":"e0058b90-36fa-4862-bf9e-32d022a6d754","resolution":{"observed_at":"2026-08-10T14:59:45.687362Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2208.04280","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T14:59:45.659859Z","title":"Molnar et al","venue":null,"work_id":"b49c0880-5177-43e3-b1b2-45639ac009c4","year":null},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.988142Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:a200b27edf8823cd82e215de4f0b831c83bd3234c326a85f3c92e1a1d6bd5c5a","observation_id":"f2c29002-6490-422c-9dec-7e8f8b4a184f","resolution":{"observed_at":"2026-08-10T14:59:45.667938Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2021.11442","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T14:59:45.578548Z","title":"Mosaic flows: A transferable deep learning framework for solving PDEs on unseen domains","venue":null,"work_id":"6685040a-7cc5-4e43-a4ec-74f6a5da0285","year":2022},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.997674Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:7a83aef4ef53f55dec360f0ba9ec45f64c6a89adec393b53a3602c5427e6fdbc","observation_id":"cee4419c-3297-488c-91b7-ba2cbcee0516","resolution":{"observed_at":"2026-08-10T14:59:45.585990Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.10289","last_updated":"2022-07-21T03:57:27Z","snapshot_observed_at":"2026-08-13T14:59:41.500423Z","submitted_at":"2022-07-21T03:57:27Z","title":"A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.10289","snapshot_observed_at":"2026-08-10T14:59:45.001829Z","title":"A comprehensive study of non-adaptive and residual-based adaptive sam- pling for physics-informed neural networks","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:45.001829Z"},"links":{"cited_paper":"/paper/2207.10289","citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:10cb622cd9360f0a0baa590034a98274c5d2a99f379911665ff12c3541a488f3","observation_id":"4a9b4062-f831-4bde-b14e-5cf79d1ac98b","resolution":{"observed_at":"2026-08-10T14:59:45.001829Z","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":"2022.11482","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T14:59:45.506163Z","title":"Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems","venue":null,"work_id":"d1e5ec10-1718-4a30-a8e6-55031bb94324","year":2022},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:45.006840Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:fb68908a9dbbef26c28a46584f5b9e43d43d7ccdde8b86132f54593f49aed0c0","observation_id":"7e129934-768b-4e00-8e71-6f5e1f1d68de","resolution":{"observed_at":"2026-08-10T14:59:45.514141Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T14:59:46.570605Z","title":null,"venue":null,"work_id":"5903b03f-5c16-4e14-b952-24bcf445595b","year":null},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:45.011217Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:0bca768287fe085a9027784bf2004b955f0987e6793cf9f72f0b53063fd943ab","observation_id":"8d29a4be-48ec-46a3-9478-cdf40e363b2b","resolution":{"observed_at":"2026-08-10T14:59:46.575431Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.2514/6.2022-1437","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T14:59:45.066195Z","title":"Accuracy Improvement Technique of DNN for Accelerating CFD Simu- lator","venue":null,"work_id":"1aa21420-c797-4213-a7d3-6ddef82b2f40","year":2022},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:45.015437Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:aaca70d40b1b69c7e26851e7ff27b3b9d413b59d172a06c4bdf48cb8f5acd7b2","observation_id":"6bb45c3a-939a-4142-a3ca-52c0c26c3044","resolution":{"observed_at":"2026-08-10T14:59:45.072473Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T14:59:46.555624Z","title":"url: https://how5.cenaero.be/ content/vl1-laminar-joukowski-airfoil-re1000","venue":null,"work_id":"01804082-fe25-4c98-b78c-ad93ed210670","year":null},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:45.019659Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:6a3a3ef0a2da41f8530086fffe859c319647c61d7138f6e354a7d4b4a1728c46","observation_id":"96fca9a3-57a1-470f-af3b-0ee71f6937ac","resolution":{"observed_at":"2026-08-10T14:59:46.560613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-10T14:59:45.023603Z","title":"Fast Neural Network Predictions from Constrained Aerodynamics Datasets","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:45.023603Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:6be077827c0bdc981ee52bad86253654d36647f140241b230c7309838da4fb78","observation_id":"f4a6ddbf-1d6e-4c63-b998-93833fc80462","resolution":{"observed_at":"2026-08-10T14:59:45.023603Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.2514/6.2017-1306","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T14:59:45.417169Z","title":"doi: 10.2514/6.2017-1306","venue":null,"work_id":"6b028ba0-2b44-4ae9-8273-94f9bd354c3c","year":2017},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.809201Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:86f38486875f7c07b05d25c866cf39b72e57d48153343cd1643a285d67d3969b","observation_id":"230a7d86-7535-4927-9024-a5cfd0779a3e","resolution":{"observed_at":"2026-08-10T14:59:45.421193Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.48550/arxiv.2208.04280","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T14:59:45.260890Z","title":"url: http://arxiv.org/abs/2208.04280","venue":null,"work_id":"9ae6ddca-9166-4056-84fb-31f7724d66e9","year":null},"citing_paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:44.993060Z"},"links":{"citing_paper":"/paper/2501.14870"},"observation_digest":"sha256:3f86096e711a21eaf37528f497d4b458e9c3c308b3a82ab5d8604ce768657dc8","observation_id":"2615a603-0a51-48f2-bac3-4d0b40725b85","resolution":{"observed_at":"2026-08-10T14:59:45.265379Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.14870","last_updated":"2025-01-24T19:00:24Z","latest_version":1,"primary_category":"physics.flu-dyn","snapshot_observed_at":"2026-08-13T09:51:57.437971Z","submitted_at":"2025-01-24T19:00:24Z","title":"Multi-Fidelity Machine Learning Applied to Steady Fluid Flows"},"reference_resolution":{"displayed":59,"state_counts":{"malformed_identifier":6,"metadata_mismatch":3,"parse_uncertain":0,"unresolved":25,"verified_exact":8,"verified_fuzzy":17},"total_outbound_references":59},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 0 inbound Pith citation observations for arXiv:2501.14870."}