{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:G44B4IOOWXQZOKXD72F5EIMBK2","short_pith_number":"pith:G44B4IOO","schema_version":"1.0","canonical_sha256":"37381e21ceb5e1972ae3fe8bd2218156ab413122af6ba54d812a4d735a1224f6","source":{"kind":"arxiv","id":"2607.24534","version":1},"attestation_state":"computed","paper":{"title":"Amortized Posteriors for Estimation of Material Constitutive Parameters from Multimodal Measurements on Small Punch Tests","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.mtrl-sci"],"primary_cat":"cond-mat.stat-mech","authors_text":"Aditya Venkatraman, Mohammad Ali Seyed Mahmoud, Raj Mahat, Samantha Mitra, Surya R. Kalidindi","submitted_at":"2026-07-27T15:11:22Z","abstract_excerpt":"Bayesian calibration of material constitutive parameters from multimodal mechanical test data is often limited by the need to specify a joint likelihood across measurement modalities that differ in dimensionality, noise structure, and physical units. The resulting posteriors are often broad or strongly correlated, causing standard Markov Chain Monte Carlo (MCMC) samplers to mix poorly. Here, we present an amortized, likelihood-free framework that combines Gaussian process (GP) surrogates with Conditional Flow Matching (CFM) to learn conditional posteriors over constitutive parameters directly "},"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":"2607.24534","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.stat-mech","submitted_at":"2026-07-27T15:11:22Z","cross_cats_sorted":["cond-mat.mtrl-sci"],"title_canon_sha256":"69f5a0ef50dc10921ea7b8545fafad57881a800a766ea69413924065da840acb","abstract_canon_sha256":"ea66b81ae0370319d279799244c0c543eaa225a56d3fb75d41e99e483787dda4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-28T02:24:08.661899Z","signature_b64":"ed6iV6E0Bzasp6DmJplh2uoyYshwMQX7jUeKStqDqdGk2u7Iiq1Zvarw4tUiZw1ZuLTgT6UWW/zXWF5vHhvhDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"37381e21ceb5e1972ae3fe8bd2218156ab413122af6ba54d812a4d735a1224f6","last_reissued_at":"2026-07-28T02:24:08.661052Z","signature_status":"signed_v1","first_computed_at":"2026-07-28T02:24:08.661052Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Amortized Posteriors for Estimation of Material Constitutive Parameters from Multimodal Measurements on Small Punch Tests","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.mtrl-sci"],"primary_cat":"cond-mat.stat-mech","authors_text":"Aditya Venkatraman, Mohammad Ali Seyed Mahmoud, Raj Mahat, Samantha Mitra, Surya R. Kalidindi","submitted_at":"2026-07-27T15:11:22Z","abstract_excerpt":"Bayesian calibration of material constitutive parameters from multimodal mechanical test data is often limited by the need to specify a joint likelihood across measurement modalities that differ in dimensionality, noise structure, and physical units. The resulting posteriors are often broad or strongly correlated, causing standard Markov Chain Monte Carlo (MCMC) samplers to mix poorly. Here, we present an amortized, likelihood-free framework that combines Gaussian process (GP) surrogates with Conditional Flow Matching (CFM) to learn conditional posteriors over constitutive parameters directly "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.24534","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/2607.24534/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":"2607.24534","created_at":"2026-07-28T02:24:08.661494+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.24534v1","created_at":"2026-07-28T02:24:08.661494+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.24534","created_at":"2026-07-28T02:24:08.661494+00:00"},{"alias_kind":"pith_short_12","alias_value":"G44B4IOOWXQZ","created_at":"2026-07-28T02:24:08.661494+00:00"},{"alias_kind":"pith_short_16","alias_value":"G44B4IOOWXQZOKXD","created_at":"2026-07-28T02:24:08.661494+00:00"},{"alias_kind":"pith_short_8","alias_value":"G44B4IOO","created_at":"2026-07-28T02:24:08.661494+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/G44B4IOOWXQZOKXD72F5EIMBK2","json":"https://pith.science/pith/G44B4IOOWXQZOKXD72F5EIMBK2.json","graph_json":"https://pith.science/api/pith-number/G44B4IOOWXQZOKXD72F5EIMBK2/graph.json","events_json":"https://pith.science/api/pith-number/G44B4IOOWXQZOKXD72F5EIMBK2/events.json","paper":"https://pith.science/paper/G44B4IOO"},"agent_actions":{"view_html":"https://pith.science/pith/G44B4IOOWXQZOKXD72F5EIMBK2","download_json":"https://pith.science/pith/G44B4IOOWXQZOKXD72F5EIMBK2.json","view_paper":"https://pith.science/paper/G44B4IOO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.24534&json=true","fetch_graph":"https://pith.science/api/pith-number/G44B4IOOWXQZOKXD72F5EIMBK2/graph.json","fetch_events":"https://pith.science/api/pith-number/G44B4IOOWXQZOKXD72F5EIMBK2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G44B4IOOWXQZOKXD72F5EIMBK2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G44B4IOOWXQZOKXD72F5EIMBK2/action/storage_attestation","attest_author":"https://pith.science/pith/G44B4IOOWXQZOKXD72F5EIMBK2/action/author_attestation","sign_citation":"https://pith.science/pith/G44B4IOOWXQZOKXD72F5EIMBK2/action/citation_signature","submit_replication":"https://pith.science/pith/G44B4IOOWXQZOKXD72F5EIMBK2/action/replication_record"}},"created_at":"2026-07-28T02:24:08.661494+00:00","updated_at":"2026-07-28T02:24:08.661494+00:00"}