{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:SFZU2Q3ML5EGAJL44ILCIW2KGN","short_pith_number":"pith:SFZU2Q3M","schema_version":"1.0","canonical_sha256":"91734d436c5f4860257ce216245b4a3356729b03cd679a8f7ce00b156bc8da86","source":{"kind":"arxiv","id":"2504.06699","version":1},"attestation_state":"computed","paper":{"title":"Benchmarking Convolutional Neural Network and Graph Neural Network based Surrogate Models on a Real-World Car External Aerodynamics Dataset","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Carsten Othmer, Harald K\\\"ostler, Markus Mrosek, Sam Jacob Jacob","submitted_at":"2025-04-09T09:04:59Z","abstract_excerpt":"Aerodynamic optimization is crucial for developing eco-friendly, aerodynamic, and stylish cars, which requires close collaboration between aerodynamicists and stylists, a collaboration impaired by the time-consuming nature of aerodynamic simulations. Surrogate models offer a viable solution to reduce this overhead, but they are untested in real-world aerodynamic datasets. We present a comparative evaluation of two surrogate modeling approaches for predicting drag on a real-world dataset: a Convolutional Neural Network (CNN) model that uses a signed distance field as input and a commercial tool"},"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":"2504.06699","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-09T09:04:59Z","cross_cats_sorted":[],"title_canon_sha256":"0025d99d8a418090bfa25fbcd8f323a8f32a3799d442e1f62eec331a9c535afe","abstract_canon_sha256":"a35d65d8aa94389408bb67b724402054ec81be61619aa7a3ac4e5b83bbc012a0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:47:21.655515Z","signature_b64":"TWg9zASIS8F3TcE3Kdu5PQksKSUvxsYqFYGCLQnoyPRkHuDPkVv8e9fYj+USPqvdoLyucvMFz+39T5iGReYdAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"91734d436c5f4860257ce216245b4a3356729b03cd679a8f7ce00b156bc8da86","last_reissued_at":"2026-07-05T11:47:21.655022Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:47:21.655022Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Benchmarking Convolutional Neural Network and Graph Neural Network based Surrogate Models on a Real-World Car External Aerodynamics Dataset","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Carsten Othmer, Harald K\\\"ostler, Markus Mrosek, Sam Jacob Jacob","submitted_at":"2025-04-09T09:04:59Z","abstract_excerpt":"Aerodynamic optimization is crucial for developing eco-friendly, aerodynamic, and stylish cars, which requires close collaboration between aerodynamicists and stylists, a collaboration impaired by the time-consuming nature of aerodynamic simulations. Surrogate models offer a viable solution to reduce this overhead, but they are untested in real-world aerodynamic datasets. We present a comparative evaluation of two surrogate modeling approaches for predicting drag on a real-world dataset: a Convolutional Neural Network (CNN) model that uses a signed distance field as input and a commercial tool"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.06699","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/2504.06699/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":"2504.06699","created_at":"2026-07-05T11:47:21.655081+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.06699v1","created_at":"2026-07-05T11:47:21.655081+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.06699","created_at":"2026-07-05T11:47:21.655081+00:00"},{"alias_kind":"pith_short_12","alias_value":"SFZU2Q3ML5EG","created_at":"2026-07-05T11:47:21.655081+00:00"},{"alias_kind":"pith_short_16","alias_value":"SFZU2Q3ML5EGAJL4","created_at":"2026-07-05T11:47:21.655081+00:00"},{"alias_kind":"pith_short_8","alias_value":"SFZU2Q3M","created_at":"2026-07-05T11:47:21.655081+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.19565","citing_title":"HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics","ref_index":39,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SFZU2Q3ML5EGAJL44ILCIW2KGN","json":"https://pith.science/pith/SFZU2Q3ML5EGAJL44ILCIW2KGN.json","graph_json":"https://pith.science/api/pith-number/SFZU2Q3ML5EGAJL44ILCIW2KGN/graph.json","events_json":"https://pith.science/api/pith-number/SFZU2Q3ML5EGAJL44ILCIW2KGN/events.json","paper":"https://pith.science/paper/SFZU2Q3M"},"agent_actions":{"view_html":"https://pith.science/pith/SFZU2Q3ML5EGAJL44ILCIW2KGN","download_json":"https://pith.science/pith/SFZU2Q3ML5EGAJL44ILCIW2KGN.json","view_paper":"https://pith.science/paper/SFZU2Q3M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.06699&json=true","fetch_graph":"https://pith.science/api/pith-number/SFZU2Q3ML5EGAJL44ILCIW2KGN/graph.json","fetch_events":"https://pith.science/api/pith-number/SFZU2Q3ML5EGAJL44ILCIW2KGN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SFZU2Q3ML5EGAJL44ILCIW2KGN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SFZU2Q3ML5EGAJL44ILCIW2KGN/action/storage_attestation","attest_author":"https://pith.science/pith/SFZU2Q3ML5EGAJL44ILCIW2KGN/action/author_attestation","sign_citation":"https://pith.science/pith/SFZU2Q3ML5EGAJL44ILCIW2KGN/action/citation_signature","submit_replication":"https://pith.science/pith/SFZU2Q3ML5EGAJL44ILCIW2KGN/action/replication_record"}},"created_at":"2026-07-05T11:47:21.655081+00:00","updated_at":"2026-07-05T11:47:21.655081+00:00"}