{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:7OE4GKQIQ6PEUX3DSPVCQH4R3E","short_pith_number":"pith:7OE4GKQI","schema_version":"1.0","canonical_sha256":"fb89c32a08879e4a5f6393ea281f91d92d9aec4544674c031407d03e4302837c","source":{"kind":"arxiv","id":"2605.24506","version":1},"attestation_state":"computed","paper":{"title":"Safe Data-Driven Control and Dynamical Learning via Constrained Neural Architectures and Koopman Operators","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Lin Feng, Xin He","submitted_at":"2026-05-23T10:37:04Z","abstract_excerpt":"The deployment of learning-based models in safety-critical control systems demands mathematical guarantees that standard regression architectures cannot provide. This paper presents an integrated framework that bridges Neural Ordinary Differential Equations (Neural ODEs), measurement-induced geometric structures, and Koopman operator theory, with the explicit aim of producing data-driven models whose stability certificates are computable, not merely conjectured. Three complementary components are developed and analyzed. First, ControlSynth Neural ODEs enforce global convergence through tractab"},"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":"2605.24506","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2026-05-23T10:37:04Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"b8efc3a696a4fc367ecb63a421770dd8d5c0fd08d4353ff9f71bff90cfc262b0","abstract_canon_sha256":"97cedd2147898d71f80673246fc58f4090fd488b9ca3dc687757d15499b69b57"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T01:03:43.490104Z","signature_b64":"s4r6Zy/0Qf9rJ3rryceYIi6urSdKqW7E9Nx5e+B+G6Aobl5UNG6oM8Lo4jrT3sv69I3EXu0TV2+gy+d3VF57Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fb89c32a08879e4a5f6393ea281f91d92d9aec4544674c031407d03e4302837c","last_reissued_at":"2026-05-26T01:03:43.489339Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T01:03:43.489339Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Safe Data-Driven Control and Dynamical Learning via Constrained Neural Architectures and Koopman Operators","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Lin Feng, Xin He","submitted_at":"2026-05-23T10:37:04Z","abstract_excerpt":"The deployment of learning-based models in safety-critical control systems demands mathematical guarantees that standard regression architectures cannot provide. This paper presents an integrated framework that bridges Neural Ordinary Differential Equations (Neural ODEs), measurement-induced geometric structures, and Koopman operator theory, with the explicit aim of producing data-driven models whose stability certificates are computable, not merely conjectured. Three complementary components are developed and analyzed. First, ControlSynth Neural ODEs enforce global convergence through tractab"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.24506","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/2605.24506/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":"2605.24506","created_at":"2026-05-26T01:03:43.489473+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.24506v1","created_at":"2026-05-26T01:03:43.489473+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.24506","created_at":"2026-05-26T01:03:43.489473+00:00"},{"alias_kind":"pith_short_12","alias_value":"7OE4GKQIQ6PE","created_at":"2026-05-26T01:03:43.489473+00:00"},{"alias_kind":"pith_short_16","alias_value":"7OE4GKQIQ6PEUX3D","created_at":"2026-05-26T01:03:43.489473+00:00"},{"alias_kind":"pith_short_8","alias_value":"7OE4GKQI","created_at":"2026-05-26T01:03:43.489473+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/7OE4GKQIQ6PEUX3DSPVCQH4R3E","json":"https://pith.science/pith/7OE4GKQIQ6PEUX3DSPVCQH4R3E.json","graph_json":"https://pith.science/api/pith-number/7OE4GKQIQ6PEUX3DSPVCQH4R3E/graph.json","events_json":"https://pith.science/api/pith-number/7OE4GKQIQ6PEUX3DSPVCQH4R3E/events.json","paper":"https://pith.science/paper/7OE4GKQI"},"agent_actions":{"view_html":"https://pith.science/pith/7OE4GKQIQ6PEUX3DSPVCQH4R3E","download_json":"https://pith.science/pith/7OE4GKQIQ6PEUX3DSPVCQH4R3E.json","view_paper":"https://pith.science/paper/7OE4GKQI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.24506&json=true","fetch_graph":"https://pith.science/api/pith-number/7OE4GKQIQ6PEUX3DSPVCQH4R3E/graph.json","fetch_events":"https://pith.science/api/pith-number/7OE4GKQIQ6PEUX3DSPVCQH4R3E/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7OE4GKQIQ6PEUX3DSPVCQH4R3E/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7OE4GKQIQ6PEUX3DSPVCQH4R3E/action/storage_attestation","attest_author":"https://pith.science/pith/7OE4GKQIQ6PEUX3DSPVCQH4R3E/action/author_attestation","sign_citation":"https://pith.science/pith/7OE4GKQIQ6PEUX3DSPVCQH4R3E/action/citation_signature","submit_replication":"https://pith.science/pith/7OE4GKQIQ6PEUX3DSPVCQH4R3E/action/replication_record"}},"created_at":"2026-05-26T01:03:43.489473+00:00","updated_at":"2026-05-26T01:03:43.489473+00:00"}