{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SD6ZCNROPJPFDGBAA4IYF3WZLH","short_pith_number":"pith:SD6ZCNRO","schema_version":"1.0","canonical_sha256":"90fd91362e7a5e519820071182eed959c1620f8a7b072ffd6c0d3b775ed808ad","source":{"kind":"arxiv","id":"2403.01005","version":1},"attestation_state":"computed","paper":{"title":"Policy Optimization for PDE Control with a Warm Start","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.SY","math.OC"],"primary_cat":"eess.SY","authors_text":"Mouhacine Benosman, Saviz Mowlavi, Tamer Ba\\c{s}ar, Xiangyuan Zhang","submitted_at":"2024-03-01T22:03:22Z","abstract_excerpt":"Dimensionality reduction is crucial for controlling nonlinear partial differential equations (PDE) through a \"reduce-then-design\" strategy, which identifies a reduced-order model and then implements model-based control solutions. However, inaccuracies in the reduced-order modeling can substantially degrade controller performance, especially in PDEs with chaotic behavior. To address this issue, we augment the reduce-then-design procedure with a policy optimization (PO) step. The PO step fine-tunes the model-based controller to compensate for the modeling error from dimensionality reduction. Thi"},"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":"2403.01005","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2024-03-01T22:03:22Z","cross_cats_sorted":["cs.AI","cs.SY","math.OC"],"title_canon_sha256":"ed3c63c609bf50f1b9b29a14bc89ed265c364b9e94815d91850481020fbe060d","abstract_canon_sha256":"a2833f6e8707f0ecc91447dbab5dd69d7b21b93bf55728056037ab5c51ecba2b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:51:15.134353Z","signature_b64":"BWa45KSr7m0RBcdu1b8ea6M5xB8tr+PYFt1AuPC64WMJ+mGcfy+qtuKsHQBTCxh/KXX34r/evbh+McIo5g1FCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"90fd91362e7a5e519820071182eed959c1620f8a7b072ffd6c0d3b775ed808ad","last_reissued_at":"2026-07-05T07:51:15.133937Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:51:15.133937Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Policy Optimization for PDE Control with a Warm Start","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.SY","math.OC"],"primary_cat":"eess.SY","authors_text":"Mouhacine Benosman, Saviz Mowlavi, Tamer Ba\\c{s}ar, Xiangyuan Zhang","submitted_at":"2024-03-01T22:03:22Z","abstract_excerpt":"Dimensionality reduction is crucial for controlling nonlinear partial differential equations (PDE) through a \"reduce-then-design\" strategy, which identifies a reduced-order model and then implements model-based control solutions. However, inaccuracies in the reduced-order modeling can substantially degrade controller performance, especially in PDEs with chaotic behavior. To address this issue, we augment the reduce-then-design procedure with a policy optimization (PO) step. The PO step fine-tunes the model-based controller to compensate for the modeling error from dimensionality reduction. Thi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.01005","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/2403.01005/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":"2403.01005","created_at":"2026-07-05T07:51:15.134000+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.01005v1","created_at":"2026-07-05T07:51:15.134000+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.01005","created_at":"2026-07-05T07:51:15.134000+00:00"},{"alias_kind":"pith_short_12","alias_value":"SD6ZCNROPJPF","created_at":"2026-07-05T07:51:15.134000+00:00"},{"alias_kind":"pith_short_16","alias_value":"SD6ZCNROPJPFDGBA","created_at":"2026-07-05T07:51:15.134000+00:00"},{"alias_kind":"pith_short_8","alias_value":"SD6ZCNRO","created_at":"2026-07-05T07:51:15.134000+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.21684","citing_title":"A Dual Ensemble Kalman Filter Approach to Robust Control of Nonlinear Systems: An Application to Partial Differential Equations","ref_index":32,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SD6ZCNROPJPFDGBAA4IYF3WZLH","json":"https://pith.science/pith/SD6ZCNROPJPFDGBAA4IYF3WZLH.json","graph_json":"https://pith.science/api/pith-number/SD6ZCNROPJPFDGBAA4IYF3WZLH/graph.json","events_json":"https://pith.science/api/pith-number/SD6ZCNROPJPFDGBAA4IYF3WZLH/events.json","paper":"https://pith.science/paper/SD6ZCNRO"},"agent_actions":{"view_html":"https://pith.science/pith/SD6ZCNROPJPFDGBAA4IYF3WZLH","download_json":"https://pith.science/pith/SD6ZCNROPJPFDGBAA4IYF3WZLH.json","view_paper":"https://pith.science/paper/SD6ZCNRO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.01005&json=true","fetch_graph":"https://pith.science/api/pith-number/SD6ZCNROPJPFDGBAA4IYF3WZLH/graph.json","fetch_events":"https://pith.science/api/pith-number/SD6ZCNROPJPFDGBAA4IYF3WZLH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SD6ZCNROPJPFDGBAA4IYF3WZLH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SD6ZCNROPJPFDGBAA4IYF3WZLH/action/storage_attestation","attest_author":"https://pith.science/pith/SD6ZCNROPJPFDGBAA4IYF3WZLH/action/author_attestation","sign_citation":"https://pith.science/pith/SD6ZCNROPJPFDGBAA4IYF3WZLH/action/citation_signature","submit_replication":"https://pith.science/pith/SD6ZCNROPJPFDGBAA4IYF3WZLH/action/replication_record"}},"created_at":"2026-07-05T07:51:15.134000+00:00","updated_at":"2026-07-05T07:51:15.134000+00:00"}