{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ARCYUJ3UPK4QO5MH6TMW7FXKTF","short_pith_number":"pith:ARCYUJ3U","schema_version":"1.0","canonical_sha256":"04458a27747ab9077587f4d96f96ea99417a618c02a3bb55ee11bd2b8b4b649a","source":{"kind":"arxiv","id":"2403.01623","version":1},"attestation_state":"computed","paper":{"title":"ML4PhySim : Machine Learning for Physical Simulations Challenge (The airfoil design)","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CE"],"primary_cat":"cs.LG","authors_text":"Asma Farjallah, David Danan, Florent Bonnet, Jean-Patrick Brunet, Jocelyn Ahmed Mazari, Marc Schoenauer, Milad Leyli-Abadi, Mouadh Yagoubi, Patrick Gallinari","submitted_at":"2024-03-03T22:10:21Z","abstract_excerpt":"The use of machine learning (ML) techniques to solve complex physical problems has been considered recently as a promising approach. However, the evaluation of such learned physical models remains an important issue for industrial use. The aim of this competition is to encourage the development of new ML techniques to solve physical problems using a unified evaluation framework proposed recently, called Learning Industrial Physical Simulations (LIPS). We propose learning a task representing a well-known physical use case: the airfoil design simulation, using a dataset called AirfRANS. The glob"},"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":"2403.01623","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-03T22:10:21Z","cross_cats_sorted":["cs.CE"],"title_canon_sha256":"13d3f4c41db6d4d4860adda1ec94a91fb719ce905c87b0040abd52f587624629","abstract_canon_sha256":"4f259f6d0597555a28eb929519362f976534d8f2b4058e8444987227fb2e96fc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:51:49.979659Z","signature_b64":"bqY+Gn0djl9aJU0xeBizSU0ZgE4eydgyOu7j5qWhwUUCUibd01iP50HQ8OiVyw5tHf36ByVmYy/UcTSfitNWBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"04458a27747ab9077587f4d96f96ea99417a618c02a3bb55ee11bd2b8b4b649a","last_reissued_at":"2026-07-05T07:51:49.979218Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:51:49.979218Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ML4PhySim : Machine Learning for Physical Simulations Challenge (The airfoil design)","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CE"],"primary_cat":"cs.LG","authors_text":"Asma Farjallah, David Danan, Florent Bonnet, Jean-Patrick Brunet, Jocelyn Ahmed Mazari, Marc Schoenauer, Milad Leyli-Abadi, Mouadh Yagoubi, Patrick Gallinari","submitted_at":"2024-03-03T22:10:21Z","abstract_excerpt":"The use of machine learning (ML) techniques to solve complex physical problems has been considered recently as a promising approach. However, the evaluation of such learned physical models remains an important issue for industrial use. The aim of this competition is to encourage the development of new ML techniques to solve physical problems using a unified evaluation framework proposed recently, called Learning Industrial Physical Simulations (LIPS). We propose learning a task representing a well-known physical use case: the airfoil design simulation, using a dataset called AirfRANS. The glob"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.01623","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/2403.01623/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":"2403.01623","created_at":"2026-07-05T07:51:49.979274+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.01623v1","created_at":"2026-07-05T07:51:49.979274+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.01623","created_at":"2026-07-05T07:51:49.979274+00:00"},{"alias_kind":"pith_short_12","alias_value":"ARCYUJ3UPK4Q","created_at":"2026-07-05T07:51:49.979274+00:00"},{"alias_kind":"pith_short_16","alias_value":"ARCYUJ3UPK4QO5MH","created_at":"2026-07-05T07:51:49.979274+00:00"},{"alias_kind":"pith_short_8","alias_value":"ARCYUJ3U","created_at":"2026-07-05T07:51:49.979274+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.08516","citing_title":"NeurIPS 2024 ML4CFD Competition: Results and Retrospective Analysis","ref_index":33,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ARCYUJ3UPK4QO5MH6TMW7FXKTF","json":"https://pith.science/pith/ARCYUJ3UPK4QO5MH6TMW7FXKTF.json","graph_json":"https://pith.science/api/pith-number/ARCYUJ3UPK4QO5MH6TMW7FXKTF/graph.json","events_json":"https://pith.science/api/pith-number/ARCYUJ3UPK4QO5MH6TMW7FXKTF/events.json","paper":"https://pith.science/paper/ARCYUJ3U"},"agent_actions":{"view_html":"https://pith.science/pith/ARCYUJ3UPK4QO5MH6TMW7FXKTF","download_json":"https://pith.science/pith/ARCYUJ3UPK4QO5MH6TMW7FXKTF.json","view_paper":"https://pith.science/paper/ARCYUJ3U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.01623&json=true","fetch_graph":"https://pith.science/api/pith-number/ARCYUJ3UPK4QO5MH6TMW7FXKTF/graph.json","fetch_events":"https://pith.science/api/pith-number/ARCYUJ3UPK4QO5MH6TMW7FXKTF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ARCYUJ3UPK4QO5MH6TMW7FXKTF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ARCYUJ3UPK4QO5MH6TMW7FXKTF/action/storage_attestation","attest_author":"https://pith.science/pith/ARCYUJ3UPK4QO5MH6TMW7FXKTF/action/author_attestation","sign_citation":"https://pith.science/pith/ARCYUJ3UPK4QO5MH6TMW7FXKTF/action/citation_signature","submit_replication":"https://pith.science/pith/ARCYUJ3UPK4QO5MH6TMW7FXKTF/action/replication_record"}},"created_at":"2026-07-05T07:51:49.979274+00:00","updated_at":"2026-07-05T07:51:49.979274+00:00"}