{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:3GJOU2V3YOCUWAZXNM4NFY2K4Y","short_pith_number":"pith:3GJOU2V3","schema_version":"1.0","canonical_sha256":"d992ea6abbc3854b03376b38d2e34ae63e3e990c5fdbd060f4cff4ba40a9bead","source":{"kind":"arxiv","id":"2102.06627","version":2},"attestation_state":"computed","paper":{"title":"An efficient method for goal-oriented linear Bayesian optimal experimental design: Application to optimal sensor placemen","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NA","math.NA"],"primary_cat":"math.OC","authors_text":"Keyi Wu, Omar Ghattas, Peng Chen","submitted_at":"2021-02-12T17:13:18Z","abstract_excerpt":"Optimal experimental design (OED) plays an important role in the problem of identifying uncertainty with limited experimental data. In many applications, we seek to minimize the uncertainty of a predicted quantity of interest (QoI) based on the solution of the inverse problem, rather than the inversion model parameter itself. In these scenarios, we develop an efficient method for goal-oriented optimal experimental design (GOOED) for large-scale Bayesian linear inverse problem that finds sensor locations to maximize the expected information gain (EIG) for a predicted QoI. By deriving a new form"},"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":"2102.06627","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2021-02-12T17:13:18Z","cross_cats_sorted":["cs.NA","math.NA"],"title_canon_sha256":"20ab1acf58d383bccb504d0e74142dd0a6b7288c373a9967f77c588cd07cb2d6","abstract_canon_sha256":"b63091422925ec9e5eef9166f463bab77489274062608cbb09c16c285718d6bf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:46:12.932845Z","signature_b64":"yFaV8QHuxZBv/P3dAWVUVLCYS+9wH7JnzmnD1vbwsl3pjuKmAyAdDDiqqDeV97Szo8YvmuShxa9NOb3L+b9TAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d992ea6abbc3854b03376b38d2e34ae63e3e990c5fdbd060f4cff4ba40a9bead","last_reissued_at":"2026-07-05T03:46:12.932446Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:46:12.932446Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An efficient method for goal-oriented linear Bayesian optimal experimental design: Application to optimal sensor placemen","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NA","math.NA"],"primary_cat":"math.OC","authors_text":"Keyi Wu, Omar Ghattas, Peng Chen","submitted_at":"2021-02-12T17:13:18Z","abstract_excerpt":"Optimal experimental design (OED) plays an important role in the problem of identifying uncertainty with limited experimental data. In many applications, we seek to minimize the uncertainty of a predicted quantity of interest (QoI) based on the solution of the inverse problem, rather than the inversion model parameter itself. In these scenarios, we develop an efficient method for goal-oriented optimal experimental design (GOOED) for large-scale Bayesian linear inverse problem that finds sensor locations to maximize the expected information gain (EIG) for a predicted QoI. By deriving a new form"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.06627","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/2102.06627/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":"2102.06627","created_at":"2026-07-05T03:46:12.932503+00:00"},{"alias_kind":"arxiv_version","alias_value":"2102.06627v2","created_at":"2026-07-05T03:46:12.932503+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.06627","created_at":"2026-07-05T03:46:12.932503+00:00"},{"alias_kind":"pith_short_12","alias_value":"3GJOU2V3YOCU","created_at":"2026-07-05T03:46:12.932503+00:00"},{"alias_kind":"pith_short_16","alias_value":"3GJOU2V3YOCUWAZX","created_at":"2026-07-05T03:46:12.932503+00:00"},{"alias_kind":"pith_short_8","alias_value":"3GJOU2V3","created_at":"2026-07-05T03:46:12.932503+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2403.18072","citing_title":"Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models using Markov Chain Monte Carlo","ref_index":57,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3GJOU2V3YOCUWAZXNM4NFY2K4Y","json":"https://pith.science/pith/3GJOU2V3YOCUWAZXNM4NFY2K4Y.json","graph_json":"https://pith.science/api/pith-number/3GJOU2V3YOCUWAZXNM4NFY2K4Y/graph.json","events_json":"https://pith.science/api/pith-number/3GJOU2V3YOCUWAZXNM4NFY2K4Y/events.json","paper":"https://pith.science/paper/3GJOU2V3"},"agent_actions":{"view_html":"https://pith.science/pith/3GJOU2V3YOCUWAZXNM4NFY2K4Y","download_json":"https://pith.science/pith/3GJOU2V3YOCUWAZXNM4NFY2K4Y.json","view_paper":"https://pith.science/paper/3GJOU2V3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2102.06627&json=true","fetch_graph":"https://pith.science/api/pith-number/3GJOU2V3YOCUWAZXNM4NFY2K4Y/graph.json","fetch_events":"https://pith.science/api/pith-number/3GJOU2V3YOCUWAZXNM4NFY2K4Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3GJOU2V3YOCUWAZXNM4NFY2K4Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3GJOU2V3YOCUWAZXNM4NFY2K4Y/action/storage_attestation","attest_author":"https://pith.science/pith/3GJOU2V3YOCUWAZXNM4NFY2K4Y/action/author_attestation","sign_citation":"https://pith.science/pith/3GJOU2V3YOCUWAZXNM4NFY2K4Y/action/citation_signature","submit_replication":"https://pith.science/pith/3GJOU2V3YOCUWAZXNM4NFY2K4Y/action/replication_record"}},"created_at":"2026-07-05T03:46:12.932503+00:00","updated_at":"2026-07-05T03:46:12.932503+00:00"}