{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:BEZQRGFNVB5MMIR7WMJVX7JNX3","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"64bdc56d99b7d8076b7e4ae9b4f2fedecb273db52f602480046ab7c2d556b9ed","cross_cats_sorted":["cs.SY","eess.SY","math.DS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-10-24T20:55:46Z","title_canon_sha256":"7fb33c07c986c6c1b9990d565ddfa71e48d0c011f4d6584dce88d0870845c991"},"schema_version":"1.0","source":{"id":"2210.13602","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.13602","created_at":"2026-07-05T05:33:20Z"},{"alias_kind":"arxiv_version","alias_value":"2210.13602v2","created_at":"2026-07-05T05:33:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.13602","created_at":"2026-07-05T05:33:20Z"},{"alias_kind":"pith_short_12","alias_value":"BEZQRGFNVB5M","created_at":"2026-07-05T05:33:20Z"},{"alias_kind":"pith_short_16","alias_value":"BEZQRGFNVB5MMIR7","created_at":"2026-07-05T05:33:20Z"},{"alias_kind":"pith_short_8","alias_value":"BEZQRGFN","created_at":"2026-07-05T05:33:20Z"}],"graph_snapshots":[{"event_id":"sha256:b94d5e8a4f1dd82ea771a4e7c30e9fa742eab106b9dbe00c700e3888ae1f690e","target":"graph","created_at":"2026-07-05T05:33:20Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2210.13602/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper presents a Koopman lifting linearization method that is applicable to nonlinear dynamical systems having both stable and unstable regions. It is known that DMD and other standard data-driven methods face a fundamental difficulty in constructing a Koopman model when applied to unstable systems. Here we solve the problem by incorporating knowledge about a nonlinear state equation with a learning method for finding an effective set of observables. In a lifted space, stable and unstable regions are separated into independent subspaces. Based on this property, we propose to find effectiv","authors_text":"H. Harry Asada, Jerry Ng","cross_cats":["cs.SY","eess.SY","math.DS"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-10-24T20:55:46Z","title":"Learned Lifted Linearization Applied to Unstable Dynamic Systems Enabled by Koopman Direct Encoding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.13602","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:8aa278716f7658c85ab5c1f592e5074832c91da3e0f666551e7f1c7b2a15c2d3","target":"record","created_at":"2026-07-05T05:33:20Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"64bdc56d99b7d8076b7e4ae9b4f2fedecb273db52f602480046ab7c2d556b9ed","cross_cats_sorted":["cs.SY","eess.SY","math.DS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-10-24T20:55:46Z","title_canon_sha256":"7fb33c07c986c6c1b9990d565ddfa71e48d0c011f4d6584dce88d0870845c991"},"schema_version":"1.0","source":{"id":"2210.13602","kind":"arxiv","version":2}},"canonical_sha256":"09330898ada87ac6223fb3135bfd2dbeccfbace5eee3eaa0cd1918baeaa59ee3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"09330898ada87ac6223fb3135bfd2dbeccfbace5eee3eaa0cd1918baeaa59ee3","first_computed_at":"2026-07-05T05:33:20.002021Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:33:20.002021Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+Ssd3OLUDsmkkCwyH2vrArBGO8PM/xlUPUcggnjcpXE/GeB2YirLjLssoGjV2MvOL+Shn3gWzp7o8QMD41s0DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:33:20.002513Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.13602","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8aa278716f7658c85ab5c1f592e5074832c91da3e0f666551e7f1c7b2a15c2d3","sha256:b94d5e8a4f1dd82ea771a4e7c30e9fa742eab106b9dbe00c700e3888ae1f690e"],"state_sha256":"86c5fc498204084e6f0c641af6c6d8ee9ebf561a39c98cc6296a245c3f56e2d8"}