{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:7B7QMUCXN7EMSIGTTA752HPH3N","short_pith_number":"pith:7B7QMUCX","schema_version":"1.0","canonical_sha256":"f87f0650576fc8c920d3983fdd1de7db54525c2630bd213546993a07f06bc59f","source":{"kind":"arxiv","id":"2501.17081","version":1},"attestation_state":"computed","paper":{"title":"Graph Transformers for inverse physics: reconstructing flows around arbitrary 2D airfoils","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CE"],"primary_cat":"cs.LG","authors_text":"Eleni Chatzi, Gregory Duth\\'e, Imad Abdallah","submitted_at":"2025-01-28T17:06:09Z","abstract_excerpt":"We introduce a Graph Transformer framework that serves as a general inverse physics engine on meshes, demonstrated through the challenging task of reconstructing aerodynamic flow fields from sparse surface measurements. While deep learning has shown promising results in forward physics simulation, inverse problems remain particularly challenging due to their ill-posed nature and the difficulty of propagating information from limited boundary observations. Our approach addresses these challenges by combining the geometric expressiveness of message-passing neural networks with the global reasoni"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2501.17081","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-28T17:06:09Z","cross_cats_sorted":["cs.AI","cs.CE"],"title_canon_sha256":"d11f3b8d71834da7906acbf03fe9a3f2e0cdc4dbdf66bfb39bac99522394e24a","abstract_canon_sha256":"388ca805f1d10300d18d05be5756a09e1bafd821382afa3dfbbc8c61ba81bb87"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:06:33.938861Z","signature_b64":"r7+rsjBPmxm3yRATpJYNFT+q5xLmGzRYFEQKBJj1szIZ/os2QtMnF4q5VnhfxwTY1Gj6JPfA7fTDWByiDBw9Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f87f0650576fc8c920d3983fdd1de7db54525c2630bd213546993a07f06bc59f","last_reissued_at":"2026-07-05T10:06:33.938305Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:06:33.938305Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Graph Transformers for inverse physics: reconstructing flows around arbitrary 2D airfoils","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CE"],"primary_cat":"cs.LG","authors_text":"Eleni Chatzi, Gregory Duth\\'e, Imad Abdallah","submitted_at":"2025-01-28T17:06:09Z","abstract_excerpt":"We introduce a Graph Transformer framework that serves as a general inverse physics engine on meshes, demonstrated through the challenging task of reconstructing aerodynamic flow fields from sparse surface measurements. While deep learning has shown promising results in forward physics simulation, inverse problems remain particularly challenging due to their ill-posed nature and the difficulty of propagating information from limited boundary observations. Our approach addresses these challenges by combining the geometric expressiveness of message-passing neural networks with the global reasoni"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.17081","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2501.17081/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2501.17081","created_at":"2026-07-05T10:06:33.938374+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.17081v1","created_at":"2026-07-05T10:06:33.938374+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.17081","created_at":"2026-07-05T10:06:33.938374+00:00"},{"alias_kind":"pith_short_12","alias_value":"7B7QMUCXN7EM","created_at":"2026-07-05T10:06:33.938374+00:00"},{"alias_kind":"pith_short_16","alias_value":"7B7QMUCXN7EMSIGT","created_at":"2026-07-05T10:06:33.938374+00:00"},{"alias_kind":"pith_short_8","alias_value":"7B7QMUCX","created_at":"2026-07-05T10:06:33.938374+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.26594","citing_title":"Multiscale Decomposition Reveals Predictable Interannual Variability and Climate Trends in Antarctic Sea Ice Loss","ref_index":52,"is_internal_anchor":false},{"citing_arxiv_id":"2509.19929","citing_title":"Geometric Autoencoder Priors for Bayesian Inversion: Learn First Observe Later","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2604.26593","citing_title":"PiGGO: Physics-Guided Learnable Graph Kalman Filters for Virtual Sensing of Nonlinear Dynamic Structures under Uncertainty","ref_index":55,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06861","citing_title":"Christoffel-DPS: Optimal sensor placement in diffusion posterior sampling for arbitrary distributions","ref_index":16,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7B7QMUCXN7EMSIGTTA752HPH3N","json":"https://pith.science/pith/7B7QMUCXN7EMSIGTTA752HPH3N.json","graph_json":"https://pith.science/api/pith-number/7B7QMUCXN7EMSIGTTA752HPH3N/graph.json","events_json":"https://pith.science/api/pith-number/7B7QMUCXN7EMSIGTTA752HPH3N/events.json","paper":"https://pith.science/paper/7B7QMUCX"},"agent_actions":{"view_html":"https://pith.science/pith/7B7QMUCXN7EMSIGTTA752HPH3N","download_json":"https://pith.science/pith/7B7QMUCXN7EMSIGTTA752HPH3N.json","view_paper":"https://pith.science/paper/7B7QMUCX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.17081&json=true","fetch_graph":"https://pith.science/api/pith-number/7B7QMUCXN7EMSIGTTA752HPH3N/graph.json","fetch_events":"https://pith.science/api/pith-number/7B7QMUCXN7EMSIGTTA752HPH3N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7B7QMUCXN7EMSIGTTA752HPH3N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7B7QMUCXN7EMSIGTTA752HPH3N/action/storage_attestation","attest_author":"https://pith.science/pith/7B7QMUCXN7EMSIGTTA752HPH3N/action/author_attestation","sign_citation":"https://pith.science/pith/7B7QMUCXN7EMSIGTTA752HPH3N/action/citation_signature","submit_replication":"https://pith.science/pith/7B7QMUCXN7EMSIGTTA752HPH3N/action/replication_record"}},"created_at":"2026-07-05T10:06:33.938374+00:00","updated_at":"2026-07-05T10:06:33.938374+00:00"}