{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ZVAOKQYUTCDEWLLFAYHIQPUKLA","short_pith_number":"pith:ZVAOKQYU","schema_version":"1.0","canonical_sha256":"cd40e5431498864b2d65060e883e8a5818a5cc34acac127666d482dd1940cde7","source":{"kind":"arxiv","id":"2510.07950","version":2},"attestation_state":"computed","paper":{"title":"Likelihood-informed Model Reduction for Bayesian Inference of Static Structural Loads","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Elisabeth Ullmann, Elizabeth Qian, Iason Papaioannou, Jakob Scheffels","submitted_at":"2025-10-09T08:46:22Z","abstract_excerpt":"Bayesian inverse problems use data to update a prior probability distribution on uncertain parameter values to a posterior distribution. Such problems arise in many structural engineering applications, but computational solution of Bayesian inverse problems is often expensive because standard solution approaches require many evaluations of the forward model mapping the parameter value to predicted observations. In many settings, this forward model is expensive because it requires the solution of a high-dimensional discretization of a partial differential equation. However, Bayesian inverse pro"},"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":"2510.07950","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2025-10-09T08:46:22Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"aecc27ba4e585b9f637622e88cbe3ac1b5541734c472a952a5d7aba9b6983a8f","abstract_canon_sha256":"f8b7784a2f39b3e7897c15b819d75071a5d97fda32c9b765b861024e74779d8c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T01:02:28.354662Z","signature_b64":"HRDOpWAWNU2KZvoas10OmwwyrGjk2cKsJQFAgADlL8lFP7knjqpfq6x8sDG7GaHbQv0yxUvpb8tjSuhzIq0rAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cd40e5431498864b2d65060e883e8a5818a5cc34acac127666d482dd1940cde7","last_reissued_at":"2026-05-26T01:02:28.353691Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T01:02:28.353691Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Likelihood-informed Model Reduction for Bayesian Inference of Static Structural Loads","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Elisabeth Ullmann, Elizabeth Qian, Iason Papaioannou, Jakob Scheffels","submitted_at":"2025-10-09T08:46:22Z","abstract_excerpt":"Bayesian inverse problems use data to update a prior probability distribution on uncertain parameter values to a posterior distribution. Such problems arise in many structural engineering applications, but computational solution of Bayesian inverse problems is often expensive because standard solution approaches require many evaluations of the forward model mapping the parameter value to predicted observations. In many settings, this forward model is expensive because it requires the solution of a high-dimensional discretization of a partial differential equation. However, Bayesian inverse pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.07950","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/2510.07950/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":"2510.07950","created_at":"2026-05-26T01:02:28.353813+00:00"},{"alias_kind":"arxiv_version","alias_value":"2510.07950v2","created_at":"2026-05-26T01:02:28.353813+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.07950","created_at":"2026-05-26T01:02:28.353813+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZVAOKQYUTCDE","created_at":"2026-05-26T01:02:28.353813+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZVAOKQYUTCDEWLLF","created_at":"2026-05-26T01:02:28.353813+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZVAOKQYU","created_at":"2026-05-26T01:02:28.353813+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/ZVAOKQYUTCDEWLLFAYHIQPUKLA","json":"https://pith.science/pith/ZVAOKQYUTCDEWLLFAYHIQPUKLA.json","graph_json":"https://pith.science/api/pith-number/ZVAOKQYUTCDEWLLFAYHIQPUKLA/graph.json","events_json":"https://pith.science/api/pith-number/ZVAOKQYUTCDEWLLFAYHIQPUKLA/events.json","paper":"https://pith.science/paper/ZVAOKQYU"},"agent_actions":{"view_html":"https://pith.science/pith/ZVAOKQYUTCDEWLLFAYHIQPUKLA","download_json":"https://pith.science/pith/ZVAOKQYUTCDEWLLFAYHIQPUKLA.json","view_paper":"https://pith.science/paper/ZVAOKQYU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2510.07950&json=true","fetch_graph":"https://pith.science/api/pith-number/ZVAOKQYUTCDEWLLFAYHIQPUKLA/graph.json","fetch_events":"https://pith.science/api/pith-number/ZVAOKQYUTCDEWLLFAYHIQPUKLA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZVAOKQYUTCDEWLLFAYHIQPUKLA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZVAOKQYUTCDEWLLFAYHIQPUKLA/action/storage_attestation","attest_author":"https://pith.science/pith/ZVAOKQYUTCDEWLLFAYHIQPUKLA/action/author_attestation","sign_citation":"https://pith.science/pith/ZVAOKQYUTCDEWLLFAYHIQPUKLA/action/citation_signature","submit_replication":"https://pith.science/pith/ZVAOKQYUTCDEWLLFAYHIQPUKLA/action/replication_record"}},"created_at":"2026-05-26T01:02:28.353813+00:00","updated_at":"2026-05-26T01:02:28.353813+00:00"}