{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:Y2SDZSUR32C3XPGPO5UAIYCRNW","short_pith_number":"pith:Y2SDZSUR","schema_version":"1.0","canonical_sha256":"c6a43cca91de85bbbccf77680460516db53026052dad67bb984aaa9fec95fda3","source":{"kind":"arxiv","id":"2402.18337","version":1},"attestation_state":"computed","paper":{"title":"Probabilistic Bayesian optimal experimental design using conditional normalizing flows","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Felix J. Herrmann, Peng Chen, Rafael Orozco","submitted_at":"2024-02-28T13:59:20Z","abstract_excerpt":"Bayesian optimal experimental design (OED) seeks to conduct the most informative experiment under budget constraints to update the prior knowledge of a system to its posterior from the experimental data in a Bayesian framework. Such problems are computationally challenging because of (1) expensive and repeated evaluation of some optimality criterion that typically involves a double integration with respect to both the system parameters and the experimental data, (2) suffering from the curse-of-dimensionality when the system parameters and design variables are high-dimensional, (3) the optimiza"},"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":"2402.18337","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-28T13:59:20Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"f487bb094ea00da62b83941a1df31a54e3c251ee2d822e6f81371954a3c0a7a3","abstract_canon_sha256":"37bd73640e3af865dbf38e27e9a17045b60ac3e18b712b50688c836b5c0f3a8a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:50:15.316049Z","signature_b64":"An9Fx7caUiE+k5BF3Ywm+zvXb/KthxhZuUHrY6iJ0mySQyMcfgsiTKUzuKhiU7odVLCOmyQkdhhSJvi/gZE5Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c6a43cca91de85bbbccf77680460516db53026052dad67bb984aaa9fec95fda3","last_reissued_at":"2026-07-05T07:50:15.315703Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:50:15.315703Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Probabilistic Bayesian optimal experimental design using conditional normalizing flows","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Felix J. Herrmann, Peng Chen, Rafael Orozco","submitted_at":"2024-02-28T13:59:20Z","abstract_excerpt":"Bayesian optimal experimental design (OED) seeks to conduct the most informative experiment under budget constraints to update the prior knowledge of a system to its posterior from the experimental data in a Bayesian framework. Such problems are computationally challenging because of (1) expensive and repeated evaluation of some optimality criterion that typically involves a double integration with respect to both the system parameters and the experimental data, (2) suffering from the curse-of-dimensionality when the system parameters and design variables are high-dimensional, (3) the optimiza"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.18337","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/2402.18337/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":"2402.18337","created_at":"2026-07-05T07:50:15.315760+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.18337v1","created_at":"2026-07-05T07:50:15.315760+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.18337","created_at":"2026-07-05T07:50:15.315760+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y2SDZSUR32C3","created_at":"2026-07-05T07:50:15.315760+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y2SDZSUR32C3XPGP","created_at":"2026-07-05T07:50:15.315760+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y2SDZSUR","created_at":"2026-07-05T07:50:15.315760+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2407.16212","citing_title":"Optimal experimental design: Formulations and computations","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2409.15906","citing_title":"Local sensitivity-preserving random data down-sampling for experimental design","ref_index":30,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Y2SDZSUR32C3XPGPO5UAIYCRNW","json":"https://pith.science/pith/Y2SDZSUR32C3XPGPO5UAIYCRNW.json","graph_json":"https://pith.science/api/pith-number/Y2SDZSUR32C3XPGPO5UAIYCRNW/graph.json","events_json":"https://pith.science/api/pith-number/Y2SDZSUR32C3XPGPO5UAIYCRNW/events.json","paper":"https://pith.science/paper/Y2SDZSUR"},"agent_actions":{"view_html":"https://pith.science/pith/Y2SDZSUR32C3XPGPO5UAIYCRNW","download_json":"https://pith.science/pith/Y2SDZSUR32C3XPGPO5UAIYCRNW.json","view_paper":"https://pith.science/paper/Y2SDZSUR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.18337&json=true","fetch_graph":"https://pith.science/api/pith-number/Y2SDZSUR32C3XPGPO5UAIYCRNW/graph.json","fetch_events":"https://pith.science/api/pith-number/Y2SDZSUR32C3XPGPO5UAIYCRNW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y2SDZSUR32C3XPGPO5UAIYCRNW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y2SDZSUR32C3XPGPO5UAIYCRNW/action/storage_attestation","attest_author":"https://pith.science/pith/Y2SDZSUR32C3XPGPO5UAIYCRNW/action/author_attestation","sign_citation":"https://pith.science/pith/Y2SDZSUR32C3XPGPO5UAIYCRNW/action/citation_signature","submit_replication":"https://pith.science/pith/Y2SDZSUR32C3XPGPO5UAIYCRNW/action/replication_record"}},"created_at":"2026-07-05T07:50:15.315760+00:00","updated_at":"2026-07-05T07:50:15.315760+00:00"}