{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:LZJHSIKQNUSSBO7KXEHPMWOQAS","short_pith_number":"pith:LZJHSIKQ","schema_version":"1.0","canonical_sha256":"5e527921506d2520bbeab90ef659d0048ff3406b6b4ae052736bc352a9491c26","source":{"kind":"arxiv","id":"2101.04757","version":2},"attestation_state":"computed","paper":{"title":"Airfoil GAN: Encoding and Synthesizing Airfoils for Aerodynamic Shape Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CE","authors_text":"Amir Barati Farimani, Kenji Shimada, Yuyang Wang","submitted_at":"2021-01-12T21:25:45Z","abstract_excerpt":"The current design of aerodynamic shapes, like airfoils, involves computationally intensive simulations to explore the possible design space. Usually, such design relies on the prior definition of design parameters and places restrictions on synthesizing novel shapes. In this work, we propose a data-driven shape encoding and generating method, which automatically learns representations from existing airfoils and uses the learned representations to generate new airfoils. The representations are then used in the optimization of synthesized airfoil shapes based on their aerodynamic performance. O"},"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":"2101.04757","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CE","submitted_at":"2021-01-12T21:25:45Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ec6ae44441037e2fcb1beb280f885b64f51a415debe3666e7cf168552f2e4152","abstract_canon_sha256":"76b9db2ab995d11961a0c422359f1fdd42118ea4028139d2e31852c2b46f3049"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:28:11.313640Z","signature_b64":"qmm+KX/1D9hEilIoerlgefGbhkzGsWDh/RKnIPU9OKVd6QnU1gLLAZUdsfi5SCArGI+kHUnB90qxvZTlqrsWAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5e527921506d2520bbeab90ef659d0048ff3406b6b4ae052736bc352a9491c26","last_reissued_at":"2026-07-05T06:28:11.313190Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:28:11.313190Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Airfoil GAN: Encoding and Synthesizing Airfoils for Aerodynamic Shape Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CE","authors_text":"Amir Barati Farimani, Kenji Shimada, Yuyang Wang","submitted_at":"2021-01-12T21:25:45Z","abstract_excerpt":"The current design of aerodynamic shapes, like airfoils, involves computationally intensive simulations to explore the possible design space. Usually, such design relies on the prior definition of design parameters and places restrictions on synthesizing novel shapes. In this work, we propose a data-driven shape encoding and generating method, which automatically learns representations from existing airfoils and uses the learned representations to generate new airfoils. The representations are then used in the optimization of synthesized airfoil shapes based on their aerodynamic performance. O"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.04757","kind":"arxiv","version":2},"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/2101.04757/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":"2101.04757","created_at":"2026-07-05T06:28:11.313245+00:00"},{"alias_kind":"arxiv_version","alias_value":"2101.04757v2","created_at":"2026-07-05T06:28:11.313245+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.04757","created_at":"2026-07-05T06:28:11.313245+00:00"},{"alias_kind":"pith_short_12","alias_value":"LZJHSIKQNUSS","created_at":"2026-07-05T06:28:11.313245+00:00"},{"alias_kind":"pith_short_16","alias_value":"LZJHSIKQNUSSBO7K","created_at":"2026-07-05T06:28:11.313245+00:00"},{"alias_kind":"pith_short_8","alias_value":"LZJHSIKQ","created_at":"2026-07-05T06:28:11.313245+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/LZJHSIKQNUSSBO7KXEHPMWOQAS","json":"https://pith.science/pith/LZJHSIKQNUSSBO7KXEHPMWOQAS.json","graph_json":"https://pith.science/api/pith-number/LZJHSIKQNUSSBO7KXEHPMWOQAS/graph.json","events_json":"https://pith.science/api/pith-number/LZJHSIKQNUSSBO7KXEHPMWOQAS/events.json","paper":"https://pith.science/paper/LZJHSIKQ"},"agent_actions":{"view_html":"https://pith.science/pith/LZJHSIKQNUSSBO7KXEHPMWOQAS","download_json":"https://pith.science/pith/LZJHSIKQNUSSBO7KXEHPMWOQAS.json","view_paper":"https://pith.science/paper/LZJHSIKQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2101.04757&json=true","fetch_graph":"https://pith.science/api/pith-number/LZJHSIKQNUSSBO7KXEHPMWOQAS/graph.json","fetch_events":"https://pith.science/api/pith-number/LZJHSIKQNUSSBO7KXEHPMWOQAS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LZJHSIKQNUSSBO7KXEHPMWOQAS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LZJHSIKQNUSSBO7KXEHPMWOQAS/action/storage_attestation","attest_author":"https://pith.science/pith/LZJHSIKQNUSSBO7KXEHPMWOQAS/action/author_attestation","sign_citation":"https://pith.science/pith/LZJHSIKQNUSSBO7KXEHPMWOQAS/action/citation_signature","submit_replication":"https://pith.science/pith/LZJHSIKQNUSSBO7KXEHPMWOQAS/action/replication_record"}},"created_at":"2026-07-05T06:28:11.313245+00:00","updated_at":"2026-07-05T06:28:11.313245+00:00"}