{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:GVHZMYBOVZDGOTTJIMUNAPO5SY","short_pith_number":"pith:GVHZMYBO","schema_version":"1.0","canonical_sha256":"354f96602eae46674e694328d03ddd96156ac52cd5c3339917389e178780428b","source":{"kind":"arxiv","id":"1911.08751","version":3},"attestation_state":"computed","paper":{"title":"Extended Dynamic Mode Decomposition with Learned Koopman Eigenfunctions for Prediction and Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Carl Folkestad, Daniel Pastor, Igor Mezic, Joel Burdick, Maria Fonoberova, Ryan Mohr","submitted_at":"2019-11-20T07:48:17Z","abstract_excerpt":"This paper presents a novel learning framework to construct Koopman eigenfunctions for unknown, nonlinear dynamics using data gathered from experiments. The learning framework can extract spectral information from the full nonlinear dynamics by learning the eigenvalues and eigenfunctions of the associated Koopman operator. We then exploit the learned Koopman eigenfunctions to learn a lifted linear state-space model. To the best of our knowledge, our method is the first to utilize Koopman eigenfunctions as lifting functions for EDMD-based methods. We demonstrate the performance of the framework"},"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":"1911.08751","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2019-11-20T07:48:17Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"129ae8ce05604ada58aad12b8fa3d2201d0ee954ca40b8f9efb045be57f20827","abstract_canon_sha256":"019e0ae036ffb52e43a90b1e2d9828c12b2882fd666cb42ae7ebdb4de0930df9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:49:01.045124Z","signature_b64":"QGJasSPPaX4gzczPEhqafTW/Ftamp/q/PmavwksjJQdfkc02fkV83/T7ixvIawFK0KcSUasz6xhKZ2YjmOIkAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"354f96602eae46674e694328d03ddd96156ac52cd5c3339917389e178780428b","last_reissued_at":"2026-07-05T00:49:01.044498Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:49:01.044498Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Extended Dynamic Mode Decomposition with Learned Koopman Eigenfunctions for Prediction and Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Carl Folkestad, Daniel Pastor, Igor Mezic, Joel Burdick, Maria Fonoberova, Ryan Mohr","submitted_at":"2019-11-20T07:48:17Z","abstract_excerpt":"This paper presents a novel learning framework to construct Koopman eigenfunctions for unknown, nonlinear dynamics using data gathered from experiments. The learning framework can extract spectral information from the full nonlinear dynamics by learning the eigenvalues and eigenfunctions of the associated Koopman operator. We then exploit the learned Koopman eigenfunctions to learn a lifted linear state-space model. To the best of our knowledge, our method is the first to utilize Koopman eigenfunctions as lifting functions for EDMD-based methods. We demonstrate the performance of the framework"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.08751","kind":"arxiv","version":3},"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/1911.08751/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":"1911.08751","created_at":"2026-07-05T00:49:01.044565+00:00"},{"alias_kind":"arxiv_version","alias_value":"1911.08751v3","created_at":"2026-07-05T00:49:01.044565+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.08751","created_at":"2026-07-05T00:49:01.044565+00:00"},{"alias_kind":"pith_short_12","alias_value":"GVHZMYBOVZDG","created_at":"2026-07-05T00:49:01.044565+00:00"},{"alias_kind":"pith_short_16","alias_value":"GVHZMYBOVZDGOTTJ","created_at":"2026-07-05T00:49:01.044565+00:00"},{"alias_kind":"pith_short_8","alias_value":"GVHZMYBO","created_at":"2026-07-05T00:49:01.044565+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/GVHZMYBOVZDGOTTJIMUNAPO5SY","json":"https://pith.science/pith/GVHZMYBOVZDGOTTJIMUNAPO5SY.json","graph_json":"https://pith.science/api/pith-number/GVHZMYBOVZDGOTTJIMUNAPO5SY/graph.json","events_json":"https://pith.science/api/pith-number/GVHZMYBOVZDGOTTJIMUNAPO5SY/events.json","paper":"https://pith.science/paper/GVHZMYBO"},"agent_actions":{"view_html":"https://pith.science/pith/GVHZMYBOVZDGOTTJIMUNAPO5SY","download_json":"https://pith.science/pith/GVHZMYBOVZDGOTTJIMUNAPO5SY.json","view_paper":"https://pith.science/paper/GVHZMYBO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1911.08751&json=true","fetch_graph":"https://pith.science/api/pith-number/GVHZMYBOVZDGOTTJIMUNAPO5SY/graph.json","fetch_events":"https://pith.science/api/pith-number/GVHZMYBOVZDGOTTJIMUNAPO5SY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GVHZMYBOVZDGOTTJIMUNAPO5SY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GVHZMYBOVZDGOTTJIMUNAPO5SY/action/storage_attestation","attest_author":"https://pith.science/pith/GVHZMYBOVZDGOTTJIMUNAPO5SY/action/author_attestation","sign_citation":"https://pith.science/pith/GVHZMYBOVZDGOTTJIMUNAPO5SY/action/citation_signature","submit_replication":"https://pith.science/pith/GVHZMYBOVZDGOTTJIMUNAPO5SY/action/replication_record"}},"created_at":"2026-07-05T00:49:01.044565+00:00","updated_at":"2026-07-05T00:49:01.044565+00:00"}