{"as_of":"2026-08-07T19:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cafe1ad0d25d2a928631a56f9e77cd171a0680e762ddaa9e2d7b8d24dd0c00ff","coverage":[{"denominator":18,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":18,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T21:59:16.729994Z","state":"measured"},{"denominator":18,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":18,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.01057/citation-record","integrity":"/paper/2507.01057/integrity","json":"/paper/2507.01057/citation-record.json","paper":"/paper/2507.01057"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:59:18.846322Z","title":"CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences","venue":null,"work_id":"7a05bc2a-a085-4d05-895c-72df6132f71e","year":2014},"citing_paper":{"arxiv_id":"2507.01057","last_updated":"2025-06-28T13:27:07Z","snapshot_observed_at":"2026-08-06T21:53:11.903878Z","submitted_at":"2025-06-28T13:27:07Z","title":"Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T21:59:15.360226Z"},"links":{"citing_paper":"/paper/2507.01057"},"observation_digest":"sha256:e14292ac3f7a123ed1c89aecaf2c63567ec71ae349d8937a1b1ddf7471cc108b","observation_id":"12d906c9-5fc8-42eb-8bcb-9cee595559cc","resolution":{"observed_at":"2026-08-06T21:59:18.955827Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:59:15.470151Z","title":"DGM: A deep learning algorithm for solving partial differential equations","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.01057","last_updated":"2025-06-28T13:27:07Z","snapshot_observed_at":"2026-08-06T21:53:11.903878Z","submitted_at":"2025-06-28T13:27:07Z","title":"Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T21:59:15.470151Z"},"links":{"citing_paper":"/paper/2507.01057"},"observation_digest":"sha256:d09372d325597cf106230f3b6db411b537832e04fa9a5f01e9401aa482cbaace","observation_id":"7995f018-7da9-459b-a684-0c890b6fc023","resolution":{"observed_at":"2026-08-06T21:59:15.470151Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.08217","last_updated":"2020-10-19T10:38:43Z","snapshot_observed_at":"2026-07-06T07:09:15.174187Z","submitted_at":"2018-10-18T18:01:01Z","title":"Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.08217","snapshot_observed_at":"2026-08-06T21:59:15.533768Z","title":"Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.01057","last_updated":"2025-06-28T13:27:07Z","snapshot_observed_at":"2026-08-06T21:53:11.903878Z","submitted_at":"2025-06-28T13:27:07Z","title":"Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T21:59:15.533768Z"},"links":{"cited_paper":"/paper/1810.08217","citing_paper":"/paper/2507.01057"},"observation_digest":"sha256:fb62524551872a44a4ceee50ae7505ca81691cec67893daa302a06b888890722","observation_id":"72c1cbd0-91ee-4ca4-94a6-3289b15f0013","resolution":{"observed_at":"2026-08-06T21:59:15.533768Z","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":"2026-08-06T21:59:18.682250Z","title":"Mesh Optimization","venue":null,"work_id":"300e7dfd-6f86-41e2-8cda-e426566f1e43","year":1993},"citing_paper":{"arxiv_id":"2507.01057","last_updated":"2025-06-28T13:27:07Z","snapshot_observed_at":"2026-08-06T21:53:11.903878Z","submitted_at":"2025-06-28T13:27:07Z","title":"Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T21:59:15.624767Z"},"links":{"citing_paper":"/paper/2507.01057"},"observation_digest":"sha256:b3cca43043839bd131505819701f6b2ced0b10ace7092a6c5d356158e652bbf3","observation_id":"310cc5e1-b2e5-4c8c-ada3-e90938ec2809","resolution":{"observed_at":"2026-08-06T21:59:18.741506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2022.11511","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:59:17.877087Z","title":"A parallel parameterized level set topology opti- mization framework for large-scale structures with unstructured meshes","venue":null,"work_id":"2bb3a6c0-0018-4565-a07a-4bc7c7247add","year":2022},"citing_paper":{"arxiv_id":"2507.01057","last_updated":"2025-06-28T13:27:07Z","snapshot_observed_at":"2026-08-06T21:53:11.903878Z","submitted_at":"2025-06-28T13:27:07Z","title":"Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T21:59:15.692536Z"},"links":{"citing_paper":"/paper/2507.01057"},"observation_digest":"sha256:a5510a9e2c198c44b2657339cd39392c6842359bec86b3ae54d908a0f5b33888","observation_id":"ee101241-893b-4463-9cd7-ee894173f278","resolution":{"observed_at":"2026-08-06T21:59:17.935148Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.4400","last_updated":"2014-03-04T05:15:42Z","snapshot_observed_at":"2026-08-02T17:47:13.893077Z","submitted_at":"2013-12-16T15:34:13Z","title":"Network In Network","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.4400","snapshot_observed_at":"2026-08-06T21:59:15.801379Z","title":"Network In Network","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2507.01057","last_updated":"2025-06-28T13:27:07Z","snapshot_observed_at":"2026-08-06T21:53:11.903878Z","submitted_at":"2025-06-28T13:27:07Z","title":"Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T21:59:15.801379Z"},"links":{"cited_paper":"/paper/1312.4400","citing_paper":"/paper/2507.01057"},"observation_digest":"sha256:08c40c03639e081372b78791a9ddb8518b689770b3b461939384c645d0d4b3b2","observation_id":"dfd7eae6-bdf0-4eb3-ad7a-894b501713ab","resolution":{"observed_at":"2026-08-06T21:59:15.801379Z","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":"2026-08-06T21:59:15.888727Z","title":"Geometric parameters in the target matrix mesh optimization paradigm","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.01057","last_updated":"2025-06-28T13:27:07Z","snapshot_observed_at":"2026-08-06T21:53:11.903878Z","submitted_at":"2025-06-28T13:27:07Z","title":"Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T21:59:15.888727Z"},"links":{"citing_paper":"/paper/2507.01057"},"observation_digest":"sha256:fa96aaea5ada036b9693625f7d2f395dc625438a3c2f267cd76271c0ad06545a","observation_id":"0f49cb33-2eac-4e2f-9d57-966b14e40469","resolution":{"observed_at":"2026-08-06T21:59:15.888727Z","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":"10.1016/j.jpdc.2022.03.006","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:59:17.082438Z","title":"A novel neural network approach for airfoil mesh quality evaluation","venue":null,"work_id":"b0d04e62-572f-4a86-b9b4-e9c929747b1d","year":2022},"citing_paper":{"arxiv_id":"2507.01057","last_updated":"2025-06-28T13:27:07Z","snapshot_observed_at":"2026-08-06T21:53:11.903878Z","submitted_at":"2025-06-28T13:27:07Z","title":"Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T21:59:15.965011Z"},"links":{"citing_paper":"/paper/2507.01057"},"observation_digest":"sha256:cabc691c7e0adb21786b0fea4a930001544421f1e8b19c218c7a389c4617b0d6","observation_id":"a0c63245-5626-4069-8067-dbd0f806d25f","resolution":{"observed_at":"2026-08-06T21:59:17.154795Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:59:18.581795Z","title":"FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation","venue":null,"work_id":"104cee4a-2b5f-4e5e-a2c0-9f3549b4494a","year":2018},"citing_paper":{"arxiv_id":"2507.01057","last_updated":"2025-06-28T13:27:07Z","snapshot_observed_at":"2026-08-06T21:53:11.903878Z","submitted_at":"2025-06-28T13:27:07Z","title":"Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T21:59:16.090235Z"},"links":{"citing_paper":"/paper/2507.01057"},"observation_digest":"sha256:5b4e16b7c92c168ec4dbd46db5a5cb3dea2eafa0b2c089585680205040a390d4","observation_id":"0ef92fc8-1cd9-477e-b99f-22e9f91ae043","resolution":{"observed_at":"2026-08-06T21:59:18.623580Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.05384","last_updated":"2018-07-20T16:00:34Z","snapshot_observed_at":"2026-08-05T04:10:49.297613Z","submitted_at":"2018-02-15T02:07:30Z","title":"AtlasNet: A Papier-M\\^ach\\'e Approach to Learning 3D Surface Generation","version":3},"cited_work":{"arxiv_id":"1802.05384","doi":null,"metadata_source":"pith","pith_arxiv_id":"1802.05384","snapshot_observed_at":"2026-08-06T21:59:17.504085Z","title":"AtlasNet: A Papier-M\\^ach\\'e Approach to Learning 3D Surface Generation","venue":"cs.CV","work_id":"ec595621-bc4b-4ecc-8f36-5902ac87529f","year":2018},"citing_paper":{"arxiv_id":"2507.01057","last_updated":"2025-06-28T13:27:07Z","snapshot_observed_at":"2026-08-06T21:53:11.903878Z","submitted_at":"2025-06-28T13:27:07Z","title":"Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T21:59:16.190301Z"},"links":{"cited_paper":"/paper/1802.05384","citing_paper":"/paper/2507.01057"},"observation_digest":"sha256:a859254eab2a36cfc152ce78be192dea0c38948c1e92df98830ed432c836dff7","observation_id":"bc83d58c-d742-44d2-9373-eb814ea54bbc","resolution":{"observed_at":"2026-08-06T21:59:17.594197Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:59:16.259178Z","title":"In:ACM Transactions on Graphics38.4 (July 2019)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.01057","last_updated":"2025-06-28T13:27:07Z","snapshot_observed_at":"2026-08-06T21:53:11.903878Z","submitted_at":"2025-06-28T13:27:07Z","title":"Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T21:59:16.259178Z"},"links":{"citing_paper":"/paper/2507.01057"},"observation_digest":"sha256:bb5264434e6228282141064b45e7453075717d10f2eae9cca4be19927521b7ed","observation_id":"3a9dbddb-85a2-44c3-9f40-351b3fababf4","resolution":{"observed_at":"2026-08-06T21:59:16.259178Z","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":"2026-08-06T21:59:18.451872Z","title":"Neural Mesh Flow: 3D Manifold Mesh GenerationviaDiffeomorphicFlows.Tech.rep.2020.Availableat<https://kunalmgupta","venue":null,"work_id":"d21607d9-9f57-4e15-bca0-66e0d3ef7d22","year":2020},"citing_paper":{"arxiv_id":"2507.01057","last_updated":"2025-06-28T13:27:07Z","snapshot_observed_at":"2026-08-06T21:53:11.903878Z","submitted_at":"2025-06-28T13:27:07Z","title":"Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T21:59:16.346849Z"},"links":{"citing_paper":"/paper/2507.01057"},"observation_digest":"sha256:92c9610d6ca5bb573c4b72f6c092c6583ece7ce73879a2f6e791e8dc42c768da","observation_id":"fc3659b4-dc23-4d1f-87a7-44b82f4fd1df","resolution":{"observed_at":"2026-08-06T21:59:18.495552Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.01428","last_updated":"2022-12-02T20:22:15Z","snapshot_observed_at":"2026-07-06T14:26:17.430390Z","submitted_at":"2022-12-02T20:22:15Z","title":"MeshDQN: A Deep Reinforcement Learning Framework for Improving Meshes in Computational Fluid Dynamics","version":1},"cited_work":{"arxiv_id":"2212.01428","doi":null,"metadata_source":"pith","pith_arxiv_id":"2212.01428","snapshot_observed_at":"2026-08-06T21:59:17.249179Z","title":"MeshDQN: A Deep Reinforcement Learning Framework for Improving Meshes in Computational Fluid Dynamics","venue":"cs.LG","work_id":"5ff99112-1f64-45e3-a680-a717502bf646","year":2022},"citing_paper":{"arxiv_id":"2507.01057","last_updated":"2025-06-28T13:27:07Z","snapshot_observed_at":"2026-08-06T21:53:11.903878Z","submitted_at":"2025-06-28T13:27:07Z","title":"Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T21:59:16.404792Z"},"links":{"cited_paper":"/paper/2212.01428","citing_paper":"/paper/2507.01057"},"observation_digest":"sha256:cf195ab77c9656356f9eb5088fb0e1d4fde6950f56ae4a1cc8a3cb124ca2d416","observation_id":"4b7d9be5-ce8c-404d-88be-cb40a05fa4b8","resolution":{"observed_at":"2026-08-06T21:59:17.315110Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:59:18.318982Z","title":"Reinforcement Learning for Adaptive Mesh Refinement","venue":null,"work_id":"92f16d1b-8542-43c7-8ad0-cd07d16045f7","year":2023},"citing_paper":{"arxiv_id":"2507.01057","last_updated":"2025-06-28T13:27:07Z","snapshot_observed_at":"2026-08-06T21:53:11.903878Z","submitted_at":"2025-06-28T13:27:07Z","title":"Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T21:59:16.463482Z"},"links":{"citing_paper":"/paper/2507.01057"},"observation_digest":"sha256:2e02fdc25ac40caa6ee530c38d5dab5cd4aaf8cea6a90e28d2bff05941940db1","observation_id":"4b6e0bb0-8562-4cdb-ab8f-bb0a8a202689","resolution":{"observed_at":"2026-08-06T21:59:18.386614Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:59:18.223235Z","title":"Swarm Reinforcement Learning for Adaptive Mesh Refinement","venue":null,"work_id":"2c13eda1-ab3f-4774-bbfa-7d5310d66643","year":2023},"citing_paper":{"arxiv_id":"2507.01057","last_updated":"2025-06-28T13:27:07Z","snapshot_observed_at":"2026-08-06T21:53:11.903878Z","submitted_at":"2025-06-28T13:27:07Z","title":"Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T21:59:16.519640Z"},"links":{"citing_paper":"/paper/2507.01057"},"observation_digest":"sha256:a22008a78217d6d42d783b26f0e889e9e1bc0f1420204fd52a46505097decf0f","observation_id":"f9030b0a-5285-4fd2-8756-63aad1b7cc1f","resolution":{"observed_at":"2026-08-06T21:59:18.270944Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:59:16.564231Z","title":"Raissi, P","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.01057","last_updated":"2025-06-28T13:27:07Z","snapshot_observed_at":"2026-08-06T21:53:11.903878Z","submitted_at":"2025-06-28T13:27:07Z","title":"Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T21:59:16.564231Z"},"links":{"citing_paper":"/paper/2507.01057"},"observation_digest":"sha256:58572a633ff7daa618a1f6d5bdc5db8e57525367b1c1b9c517eae37299ed39f8","observation_id":"b252227a-eca2-41da-9837-12bca9971d8a","resolution":{"observed_at":"2026-08-06T21:59:16.564231Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.09546","last_updated":"2022-10-18T02:45:14Z","snapshot_observed_at":"2026-08-03T10:52:59.359297Z","submitted_at":"2022-10-18T02:45:14Z","title":"An Improved Structured Mesh Generation Method Based on Physics-informed Neural Networks","version":1},"cited_work":{"arxiv_id":"2210.09546","doi":"10.48550/arxiv.2210.09546","metadata_source":"pith","pith_arxiv_id":"2210.09546","snapshot_observed_at":"2026-08-07T06:16:28.064256Z","title":"An Improved Structured Mesh Generation Method Based on Physics-informed Neural Networks","venue":"cs.GR","work_id":"41cc9cf4-7a63-44fa-a6bd-f1fb82aee194","year":2022},"citing_paper":{"arxiv_id":"2507.01057","last_updated":"2025-06-28T13:27:07Z","snapshot_observed_at":"2026-08-06T21:53:11.903878Z","submitted_at":"2025-06-28T13:27:07Z","title":"Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T21:59:16.656869Z"},"links":{"cited_paper":"/paper/2210.09546","citing_paper":"/paper/2507.01057"},"observation_digest":"sha256:aeb0377724506d3bcd70c6f2939bff29e4a30b328245d1e312405b80731de834","observation_id":"8b4989ed-af9f-4e26-986c-a97039d5a1d0","resolution":{"observed_at":"2026-08-06T21:59:16.983460Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:59:18.083506Z","title":"Understanding the difficulty of training deep feedfor- ward neural networks","venue":null,"work_id":"5f47b834-2f56-41d9-944a-4a587c32a0a4","year":2021},"citing_paper":{"arxiv_id":"2507.01057","last_updated":"2025-06-28T13:27:07Z","snapshot_observed_at":"2026-08-06T21:53:11.903878Z","submitted_at":"2025-06-28T13:27:07Z","title":"Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T21:59:16.729994Z"},"links":{"citing_paper":"/paper/2507.01057"},"observation_digest":"sha256:b07095ffca5b5958d5e91a1a7056bda9462b5be2f3e97353e1a9681340f2097a","observation_id":"b901dee5-b1da-48c8-a286-b894bb0ec4f6","resolution":{"observed_at":"2026-08-06T21:59:18.128005Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.01057","last_updated":"2025-06-28T13:27:07Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T21:53:11.903878Z","submitted_at":"2025-06-28T13:27:07Z","title":"Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates"},"reference_resolution":{"displayed":18,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":6,"verified_exact":4,"verified_fuzzy":7},"total_outbound_references":18},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2507.01057."}