{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:BG3MUJI6F2Q7Z7GMW4XWCH5HH3","short_pith_number":"pith:BG3MUJI6","schema_version":"1.0","canonical_sha256":"09b6ca251e2ea1fcfcccb72f611fa73ede7633f0ae81bf01e96710f869a91f85","source":{"kind":"arxiv","id":"1901.07521","version":1},"attestation_state":"computed","paper":{"title":"Economically Efficient Combined Plant and Controller Design Using Batch Bayesian Optimization: Mathematical Framework and Airborne Wind Energy Case Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Ali Baheri, Chris Vermillion","submitted_at":"2019-01-22T18:52:41Z","abstract_excerpt":"We present a novel data-driven nested optimization framework that addresses the problem of coupling between plant and controller optimization. This optimization strategy is tailored towards instances where a closed-form expression for the system dynamic response is unobtainable and simulations or experiments are necessary. Specifically, Bayesian Optimization, which is a data-driven technique for finding the optimum of an unknown and expensive-to-evaluate objective function, is employed to solve a nested optimization problem. The underlying objective function is modeled by a Gaussian Process (G"},"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":"1901.07521","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2019-01-22T18:52:41Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"43da9db333ab236b35b4cc38f65b31e4e193342ab4e5c07031f73d76b16e73c0","abstract_canon_sha256":"d8541619c60b7a4f46276d2fc5c57c63194bd6f3a84ad70d076f2958a70e6be5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:51:23.033814Z","signature_b64":"2q5pLCLkQ55aR1a8AH9kPvVK7CmrdDyuJdkXTlkvd+2bknU4BcUGmC/mqhD41mv3ysh0fOYJyZFqdqQpFxa7CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"09b6ca251e2ea1fcfcccb72f611fa73ede7633f0ae81bf01e96710f869a91f85","last_reissued_at":"2026-07-05T09:51:23.033462Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:51:23.033462Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Economically Efficient Combined Plant and Controller Design Using Batch Bayesian Optimization: Mathematical Framework and Airborne Wind Energy Case Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Ali Baheri, Chris Vermillion","submitted_at":"2019-01-22T18:52:41Z","abstract_excerpt":"We present a novel data-driven nested optimization framework that addresses the problem of coupling between plant and controller optimization. This optimization strategy is tailored towards instances where a closed-form expression for the system dynamic response is unobtainable and simulations or experiments are necessary. Specifically, Bayesian Optimization, which is a data-driven technique for finding the optimum of an unknown and expensive-to-evaluate objective function, is employed to solve a nested optimization problem. The underlying objective function is modeled by a Gaussian Process (G"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.07521","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/1901.07521/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":"1901.07521","created_at":"2026-07-05T09:51:23.033527+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.07521v1","created_at":"2026-07-05T09:51:23.033527+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.07521","created_at":"2026-07-05T09:51:23.033527+00:00"},{"alias_kind":"pith_short_12","alias_value":"BG3MUJI6F2Q7","created_at":"2026-07-05T09:51:23.033527+00:00"},{"alias_kind":"pith_short_16","alias_value":"BG3MUJI6F2Q7Z7GM","created_at":"2026-07-05T09:51:23.033527+00:00"},{"alias_kind":"pith_short_8","alias_value":"BG3MUJI6","created_at":"2026-07-05T09:51:23.033527+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/BG3MUJI6F2Q7Z7GMW4XWCH5HH3","json":"https://pith.science/pith/BG3MUJI6F2Q7Z7GMW4XWCH5HH3.json","graph_json":"https://pith.science/api/pith-number/BG3MUJI6F2Q7Z7GMW4XWCH5HH3/graph.json","events_json":"https://pith.science/api/pith-number/BG3MUJI6F2Q7Z7GMW4XWCH5HH3/events.json","paper":"https://pith.science/paper/BG3MUJI6"},"agent_actions":{"view_html":"https://pith.science/pith/BG3MUJI6F2Q7Z7GMW4XWCH5HH3","download_json":"https://pith.science/pith/BG3MUJI6F2Q7Z7GMW4XWCH5HH3.json","view_paper":"https://pith.science/paper/BG3MUJI6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.07521&json=true","fetch_graph":"https://pith.science/api/pith-number/BG3MUJI6F2Q7Z7GMW4XWCH5HH3/graph.json","fetch_events":"https://pith.science/api/pith-number/BG3MUJI6F2Q7Z7GMW4XWCH5HH3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BG3MUJI6F2Q7Z7GMW4XWCH5HH3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BG3MUJI6F2Q7Z7GMW4XWCH5HH3/action/storage_attestation","attest_author":"https://pith.science/pith/BG3MUJI6F2Q7Z7GMW4XWCH5HH3/action/author_attestation","sign_citation":"https://pith.science/pith/BG3MUJI6F2Q7Z7GMW4XWCH5HH3/action/citation_signature","submit_replication":"https://pith.science/pith/BG3MUJI6F2Q7Z7GMW4XWCH5HH3/action/replication_record"}},"created_at":"2026-07-05T09:51:23.033527+00:00","updated_at":"2026-07-05T09:51:23.033527+00:00"}