{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:4AVJQISTDCZJHHNWF25Q6Y3TH5","short_pith_number":"pith:4AVJQIST","schema_version":"1.0","canonical_sha256":"e02a98225318b2939db62ebb0f63733f62e8599c04551fd2718e6a4af39b673f","source":{"kind":"arxiv","id":"2212.00866","version":2},"attestation_state":"computed","paper":{"title":"Learning Robust State Observers using Neural ODEs (longer version)","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.SY"],"primary_cat":"eess.SY","authors_text":"Keyan Miao, Konstantinos Gatsis","submitted_at":"2022-12-01T20:58:39Z","abstract_excerpt":"Relying on recent research results on Neural ODEs, this paper presents a methodology for the design of state observers for nonlinear systems based on Neural ODEs, learning Luenberger-like observers and their nonlinear extension (Kazantzis-Kravaris-Luenberger (KKL) observers) for systems with partially-known nonlinear dynamics and fully unknown nonlinear dynamics, respectively. In particular, for tuneable KKL observers, the relationship between the design of the observer and its trade-off between convergence speed and robustness is analysed and used as a basis for improving the robustness of th"},"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":"2212.00866","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2022-12-01T20:58:39Z","cross_cats_sorted":["cs.LG","cs.SY"],"title_canon_sha256":"5f19793814bf2d4bd13ddb4f7956ee56e923898e161314991f8d68d251088a8a","abstract_canon_sha256":"dc5ea95dfe588c2e19062b5354a3cd001de8df6887e832e9d2f0754595df1ded"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:11:02.241780Z","signature_b64":"B9DbqSQx2u/5c8ySJz7Dei12jzP1Da+ONtv+rWnQ4cYIG6MMEsaHxqG13FXFq8bj94I7phdR5iN/1f8r7kBgAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e02a98225318b2939db62ebb0f63733f62e8599c04551fd2718e6a4af39b673f","last_reissued_at":"2026-07-05T06:11:02.241441Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:11:02.241441Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Robust State Observers using Neural ODEs (longer version)","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.SY"],"primary_cat":"eess.SY","authors_text":"Keyan Miao, Konstantinos Gatsis","submitted_at":"2022-12-01T20:58:39Z","abstract_excerpt":"Relying on recent research results on Neural ODEs, this paper presents a methodology for the design of state observers for nonlinear systems based on Neural ODEs, learning Luenberger-like observers and their nonlinear extension (Kazantzis-Kravaris-Luenberger (KKL) observers) for systems with partially-known nonlinear dynamics and fully unknown nonlinear dynamics, respectively. In particular, for tuneable KKL observers, the relationship between the design of the observer and its trade-off between convergence speed and robustness is analysed and used as a basis for improving the robustness of th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.00866","kind":"arxiv","version":2},"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/2212.00866/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":"2212.00866","created_at":"2026-07-05T06:11:02.241495+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.00866v2","created_at":"2026-07-05T06:11:02.241495+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.00866","created_at":"2026-07-05T06:11:02.241495+00:00"},{"alias_kind":"pith_short_12","alias_value":"4AVJQISTDCZJ","created_at":"2026-07-05T06:11:02.241495+00:00"},{"alias_kind":"pith_short_16","alias_value":"4AVJQISTDCZJHHNW","created_at":"2026-07-05T06:11:02.241495+00:00"},{"alias_kind":"pith_short_8","alias_value":"4AVJQIST","created_at":"2026-07-05T06:11:02.241495+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/4AVJQISTDCZJHHNWF25Q6Y3TH5","json":"https://pith.science/pith/4AVJQISTDCZJHHNWF25Q6Y3TH5.json","graph_json":"https://pith.science/api/pith-number/4AVJQISTDCZJHHNWF25Q6Y3TH5/graph.json","events_json":"https://pith.science/api/pith-number/4AVJQISTDCZJHHNWF25Q6Y3TH5/events.json","paper":"https://pith.science/paper/4AVJQIST"},"agent_actions":{"view_html":"https://pith.science/pith/4AVJQISTDCZJHHNWF25Q6Y3TH5","download_json":"https://pith.science/pith/4AVJQISTDCZJHHNWF25Q6Y3TH5.json","view_paper":"https://pith.science/paper/4AVJQIST","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.00866&json=true","fetch_graph":"https://pith.science/api/pith-number/4AVJQISTDCZJHHNWF25Q6Y3TH5/graph.json","fetch_events":"https://pith.science/api/pith-number/4AVJQISTDCZJHHNWF25Q6Y3TH5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4AVJQISTDCZJHHNWF25Q6Y3TH5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4AVJQISTDCZJHHNWF25Q6Y3TH5/action/storage_attestation","attest_author":"https://pith.science/pith/4AVJQISTDCZJHHNWF25Q6Y3TH5/action/author_attestation","sign_citation":"https://pith.science/pith/4AVJQISTDCZJHHNWF25Q6Y3TH5/action/citation_signature","submit_replication":"https://pith.science/pith/4AVJQISTDCZJHHNWF25Q6Y3TH5/action/replication_record"}},"created_at":"2026-07-05T06:11:02.241495+00:00","updated_at":"2026-07-05T06:11:02.241495+00:00"}