{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ZR3JCMV4SWGYCEZ3V2O6VAFXWE","short_pith_number":"pith:ZR3JCMV4","schema_version":"1.0","canonical_sha256":"cc769132bc958d81133bae9dea80b7b1157b0a5f7f55a41d642a0a93e1e98eee","source":{"kind":"arxiv","id":"2305.03184","version":1},"attestation_state":"computed","paper":{"title":"A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CE"],"primary_cat":"cs.LG","authors_text":"Cristina Cavinato, Enrui Zhang, George Em Karniadakis, Jay D. Humphrey, Minglang Yin, Zongren Zou","submitted_at":"2023-05-04T22:07:27Z","abstract_excerpt":"Quantifying biomechanical properties of the human vasculature could deepen our understanding of cardiovascular diseases. Standard nonlinear regression in constitutive modeling requires considerable high-quality data and an explicit form of the constitutive model as prior knowledge. By contrast, we propose a novel approach that combines generative deep learning with Bayesian inference to efficiently infer families of constitutive relationships in data-sparse regimes. Inspired by the concept of functional priors, we develop a generative adversarial network (GAN) that incorporates a neural operat"},"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":"2305.03184","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-04T22:07:27Z","cross_cats_sorted":["cs.CE"],"title_canon_sha256":"c53db4c729b3b4625b122acf4db516d3a2141f9cd32d80c78969536a9336a989","abstract_canon_sha256":"8b20110fe4ebb16f608820bcd2aef75cfea179fd55b65d80dfbcb1c26f715838"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:53:37.419049Z","signature_b64":"gs+UelFv8VeS4YHJpRyq7F9ChbFhF3leSCHTyDq6xkJhGDCCfJKkQMl7FKLu9j+UkOsUF04XIl+ZmLhc0dlWAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cc769132bc958d81133bae9dea80b7b1157b0a5f7f55a41d642a0a93e1e98eee","last_reissued_at":"2026-07-05T06:53:37.418515Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:53:37.418515Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CE"],"primary_cat":"cs.LG","authors_text":"Cristina Cavinato, Enrui Zhang, George Em Karniadakis, Jay D. Humphrey, Minglang Yin, Zongren Zou","submitted_at":"2023-05-04T22:07:27Z","abstract_excerpt":"Quantifying biomechanical properties of the human vasculature could deepen our understanding of cardiovascular diseases. Standard nonlinear regression in constitutive modeling requires considerable high-quality data and an explicit form of the constitutive model as prior knowledge. By contrast, we propose a novel approach that combines generative deep learning with Bayesian inference to efficiently infer families of constitutive relationships in data-sparse regimes. Inspired by the concept of functional priors, we develop a generative adversarial network (GAN) that incorporates a neural operat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.03184","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/2305.03184/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":"2305.03184","created_at":"2026-07-05T06:53:37.418575+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.03184v1","created_at":"2026-07-05T06:53:37.418575+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.03184","created_at":"2026-07-05T06:53:37.418575+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZR3JCMV4SWGY","created_at":"2026-07-05T06:53:37.418575+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZR3JCMV4SWGYCEZ3","created_at":"2026-07-05T06:53:37.418575+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZR3JCMV4","created_at":"2026-07-05T06:53:37.418575+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/ZR3JCMV4SWGYCEZ3V2O6VAFXWE","json":"https://pith.science/pith/ZR3JCMV4SWGYCEZ3V2O6VAFXWE.json","graph_json":"https://pith.science/api/pith-number/ZR3JCMV4SWGYCEZ3V2O6VAFXWE/graph.json","events_json":"https://pith.science/api/pith-number/ZR3JCMV4SWGYCEZ3V2O6VAFXWE/events.json","paper":"https://pith.science/paper/ZR3JCMV4"},"agent_actions":{"view_html":"https://pith.science/pith/ZR3JCMV4SWGYCEZ3V2O6VAFXWE","download_json":"https://pith.science/pith/ZR3JCMV4SWGYCEZ3V2O6VAFXWE.json","view_paper":"https://pith.science/paper/ZR3JCMV4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.03184&json=true","fetch_graph":"https://pith.science/api/pith-number/ZR3JCMV4SWGYCEZ3V2O6VAFXWE/graph.json","fetch_events":"https://pith.science/api/pith-number/ZR3JCMV4SWGYCEZ3V2O6VAFXWE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZR3JCMV4SWGYCEZ3V2O6VAFXWE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZR3JCMV4SWGYCEZ3V2O6VAFXWE/action/storage_attestation","attest_author":"https://pith.science/pith/ZR3JCMV4SWGYCEZ3V2O6VAFXWE/action/author_attestation","sign_citation":"https://pith.science/pith/ZR3JCMV4SWGYCEZ3V2O6VAFXWE/action/citation_signature","submit_replication":"https://pith.science/pith/ZR3JCMV4SWGYCEZ3V2O6VAFXWE/action/replication_record"}},"created_at":"2026-07-05T06:53:37.418575+00:00","updated_at":"2026-07-05T06:53:37.418575+00:00"}