{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:OK626TKGE7VMGB3DL3DM3JTLRP","short_pith_number":"pith:OK626TKG","schema_version":"1.0","canonical_sha256":"72bdaf4d4627eac307635ec6cda66b8bcd9ce2653b8dd6be046c0b25766b3b3b","source":{"kind":"arxiv","id":"2401.16373","version":1},"attestation_state":"computed","paper":{"title":"Bayesian optimization as a flexible and efficient design framework for sustainable process systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.LG","authors_text":"Calvin Tsay, Joel A. Paulson","submitted_at":"2024-01-29T18:12:32Z","abstract_excerpt":"Bayesian optimization (BO) is a powerful technology for optimizing noisy expensive-to-evaluate black-box functions, with a broad range of real-world applications in science, engineering, economics, manufacturing, and beyond. In this paper, we provide an overview of recent developments, challenges, and opportunities in BO for design of next-generation process systems. After describing several motivating applications, we discuss how advanced BO methods have been developed to more efficiently tackle important problems in these applications. We conclude the paper with a summary of challenges and o"},"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":"2401.16373","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-01-29T18:12:32Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"0b87bbb5203c861e0a00dbacb7ff04cb097e35f60c5858c915e09e4e2844457c","abstract_canon_sha256":"708ab744db8a3e48ebf5132471002db14a39328558c4441b974b3a9a66776380"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:38:48.225303Z","signature_b64":"Ozrt3tGTFlCQ7Zrix2ljL6tEt0fQ6nBOj+LXaGsMIHnP0dkvcu50m9DKQVjrE+Ab9vGpsbPTJdhV6iHh0bgBDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"72bdaf4d4627eac307635ec6cda66b8bcd9ce2653b8dd6be046c0b25766b3b3b","last_reissued_at":"2026-07-05T07:38:48.224843Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:38:48.224843Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian optimization as a flexible and efficient design framework for sustainable process systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.LG","authors_text":"Calvin Tsay, Joel A. Paulson","submitted_at":"2024-01-29T18:12:32Z","abstract_excerpt":"Bayesian optimization (BO) is a powerful technology for optimizing noisy expensive-to-evaluate black-box functions, with a broad range of real-world applications in science, engineering, economics, manufacturing, and beyond. In this paper, we provide an overview of recent developments, challenges, and opportunities in BO for design of next-generation process systems. After describing several motivating applications, we discuss how advanced BO methods have been developed to more efficiently tackle important problems in these applications. We conclude the paper with a summary of challenges and o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.16373","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/2401.16373/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":"2401.16373","created_at":"2026-07-05T07:38:48.224900+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.16373v1","created_at":"2026-07-05T07:38:48.224900+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.16373","created_at":"2026-07-05T07:38:48.224900+00:00"},{"alias_kind":"pith_short_12","alias_value":"OK626TKGE7VM","created_at":"2026-07-05T07:38:48.224900+00:00"},{"alias_kind":"pith_short_16","alias_value":"OK626TKGE7VMGB3D","created_at":"2026-07-05T07:38:48.224900+00:00"},{"alias_kind":"pith_short_8","alias_value":"OK626TKG","created_at":"2026-07-05T07:38:48.224900+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2410.16893","citing_title":"Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximation","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2411.00171","citing_title":"EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization","ref_index":22,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OK626TKGE7VMGB3DL3DM3JTLRP","json":"https://pith.science/pith/OK626TKGE7VMGB3DL3DM3JTLRP.json","graph_json":"https://pith.science/api/pith-number/OK626TKGE7VMGB3DL3DM3JTLRP/graph.json","events_json":"https://pith.science/api/pith-number/OK626TKGE7VMGB3DL3DM3JTLRP/events.json","paper":"https://pith.science/paper/OK626TKG"},"agent_actions":{"view_html":"https://pith.science/pith/OK626TKGE7VMGB3DL3DM3JTLRP","download_json":"https://pith.science/pith/OK626TKGE7VMGB3DL3DM3JTLRP.json","view_paper":"https://pith.science/paper/OK626TKG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.16373&json=true","fetch_graph":"https://pith.science/api/pith-number/OK626TKGE7VMGB3DL3DM3JTLRP/graph.json","fetch_events":"https://pith.science/api/pith-number/OK626TKGE7VMGB3DL3DM3JTLRP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OK626TKGE7VMGB3DL3DM3JTLRP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OK626TKGE7VMGB3DL3DM3JTLRP/action/storage_attestation","attest_author":"https://pith.science/pith/OK626TKGE7VMGB3DL3DM3JTLRP/action/author_attestation","sign_citation":"https://pith.science/pith/OK626TKGE7VMGB3DL3DM3JTLRP/action/citation_signature","submit_replication":"https://pith.science/pith/OK626TKGE7VMGB3DL3DM3JTLRP/action/replication_record"}},"created_at":"2026-07-05T07:38:48.224900+00:00","updated_at":"2026-07-05T07:38:48.224900+00:00"}