{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:OF75I3COMIBFFWJQ7GZL2GNA4X","short_pith_number":"pith:OF75I3CO","schema_version":"1.0","canonical_sha256":"717fd46c4e620252d930f9b2bd19a0e5cbf7821caba34e442207e5668ff93c9a","source":{"kind":"arxiv","id":"2605.27968","version":1},"attestation_state":"computed","paper":{"title":"Adapting Automotive Aerodynamics Surrogates to New Vehicle Families via Transfer Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CE","authors_text":"Alok Warey, SeungHwan Keum","submitted_at":"2026-05-27T05:03:25Z","abstract_excerpt":"Deploying Scientific Machine Learning surrogates in industrial CFD workflows requires adapting pretrained models to new vehicle families without large datasets; yet whether geometric representations learned by a geometry encoder transfer to topologically distinct shapes remains unvalidated.\n  We address this through leave-one-family-out experiments on a 61.47M-parameter Transformer surrogate (AB-UPT) pretrained on four vehicle families (411 external aerodynamics cases) and adapted to the held-out fifth with only 20 samples. Three strategies are compared: Full Fine-Tuning (FFT), Lightweight Fin"},"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":"2605.27968","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CE","submitted_at":"2026-05-27T05:03:25Z","cross_cats_sorted":[],"title_canon_sha256":"d42530dfe4a22dea490575b0e62f5ff9656b60c1959be2d6b04d87a53e2ce9ac","abstract_canon_sha256":"b6c24fbee0797f2f80278ba3edf4e74cd3cc87fade9121c637b530e446fe7dba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T01:04:54.842830Z","signature_b64":"HN8b4qW0j7cv+P0iFAgv4OJT4RAXsIfcCrddXGxV5ySXJETZAvJO5rBTjq34V2QSDo9ojZ09r6DtOe+9V6NJDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"717fd46c4e620252d930f9b2bd19a0e5cbf7821caba34e442207e5668ff93c9a","last_reissued_at":"2026-05-28T01:04:54.842425Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T01:04:54.842425Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adapting Automotive Aerodynamics Surrogates to New Vehicle Families via Transfer Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CE","authors_text":"Alok Warey, SeungHwan Keum","submitted_at":"2026-05-27T05:03:25Z","abstract_excerpt":"Deploying Scientific Machine Learning surrogates in industrial CFD workflows requires adapting pretrained models to new vehicle families without large datasets; yet whether geometric representations learned by a geometry encoder transfer to topologically distinct shapes remains unvalidated.\n  We address this through leave-one-family-out experiments on a 61.47M-parameter Transformer surrogate (AB-UPT) pretrained on four vehicle families (411 external aerodynamics cases) and adapted to the held-out fifth with only 20 samples. Three strategies are compared: Full Fine-Tuning (FFT), Lightweight Fin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.27968","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/2605.27968/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":"2605.27968","created_at":"2026-05-28T01:04:54.842485+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.27968v1","created_at":"2026-05-28T01:04:54.842485+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.27968","created_at":"2026-05-28T01:04:54.842485+00:00"},{"alias_kind":"pith_short_12","alias_value":"OF75I3COMIBF","created_at":"2026-05-28T01:04:54.842485+00:00"},{"alias_kind":"pith_short_16","alias_value":"OF75I3COMIBFFWJQ","created_at":"2026-05-28T01:04:54.842485+00:00"},{"alias_kind":"pith_short_8","alias_value":"OF75I3CO","created_at":"2026-05-28T01:04:54.842485+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OF75I3COMIBFFWJQ7GZL2GNA4X","json":"https://pith.science/pith/OF75I3COMIBFFWJQ7GZL2GNA4X.json","graph_json":"https://pith.science/api/pith-number/OF75I3COMIBFFWJQ7GZL2GNA4X/graph.json","events_json":"https://pith.science/api/pith-number/OF75I3COMIBFFWJQ7GZL2GNA4X/events.json","paper":"https://pith.science/paper/OF75I3CO"},"agent_actions":{"view_html":"https://pith.science/pith/OF75I3COMIBFFWJQ7GZL2GNA4X","download_json":"https://pith.science/pith/OF75I3COMIBFFWJQ7GZL2GNA4X.json","view_paper":"https://pith.science/paper/OF75I3CO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.27968&json=true","fetch_graph":"https://pith.science/api/pith-number/OF75I3COMIBFFWJQ7GZL2GNA4X/graph.json","fetch_events":"https://pith.science/api/pith-number/OF75I3COMIBFFWJQ7GZL2GNA4X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OF75I3COMIBFFWJQ7GZL2GNA4X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OF75I3COMIBFFWJQ7GZL2GNA4X/action/storage_attestation","attest_author":"https://pith.science/pith/OF75I3COMIBFFWJQ7GZL2GNA4X/action/author_attestation","sign_citation":"https://pith.science/pith/OF75I3COMIBFFWJQ7GZL2GNA4X/action/citation_signature","submit_replication":"https://pith.science/pith/OF75I3COMIBFFWJQ7GZL2GNA4X/action/replication_record"}},"created_at":"2026-05-28T01:04:54.842485+00:00","updated_at":"2026-05-28T01:04:54.842485+00:00"}