{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UVQHUYJ6L5XQOSR6PTZWOCHCQS","short_pith_number":"pith:UVQHUYJ6","schema_version":"1.0","canonical_sha256":"a5607a613e5f6f074a3e7cf36708e28491ac11814377d47e5969a5da448d8f2f","source":{"kind":"arxiv","id":"2407.00568","version":5},"attestation_state":"computed","paper":{"title":"Divide And Conquer: Learning Chaotic Dynamical Systems With Multistep Penalty Neural Ordinary Differential Equations","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Dibyajyoti Chakraborty, Romit Maulik, Seung Whan Chung, Troy Arcomano","submitted_at":"2024-06-30T02:50:28Z","abstract_excerpt":"Forecasting high-dimensional dynamical systems is a fundamental challenge in various fields, such as geosciences and engineering. Neural Ordinary Differential Equations (NODEs), which combine the power of neural networks and numerical solvers, have emerged as a promising algorithm for forecasting complex nonlinear dynamical systems. However, classical techniques used for NODE training are ineffective for learning chaotic dynamical systems. In this work, we propose a novel NODE-training approach that allows for robust learning of chaotic dynamical systems. Our method addresses the challenges of"},"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":"2407.00568","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-30T02:50:28Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"638c7c5d86eef0e9bc2ecfab41867ad47549ed47dfa3e5197a105a9c3723f09d","abstract_canon_sha256":"8ea6dbcd0a4035468eab5b568b5f2c3d0552d5460651b5eaecb5394562feec1d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:20:44.880636Z","signature_b64":"XpbHknTbQB91Y2x9vApwv86R/SBChOmu/31oshSzxrA2L9FY0aCuvUXmK8p9gASrS8ou5z5pqVhZ1Y9QH/r0Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a5607a613e5f6f074a3e7cf36708e28491ac11814377d47e5969a5da448d8f2f","last_reissued_at":"2026-07-05T09:20:44.880184Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:20:44.880184Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Divide And Conquer: Learning Chaotic Dynamical Systems With Multistep Penalty Neural Ordinary Differential Equations","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Dibyajyoti Chakraborty, Romit Maulik, Seung Whan Chung, Troy Arcomano","submitted_at":"2024-06-30T02:50:28Z","abstract_excerpt":"Forecasting high-dimensional dynamical systems is a fundamental challenge in various fields, such as geosciences and engineering. Neural Ordinary Differential Equations (NODEs), which combine the power of neural networks and numerical solvers, have emerged as a promising algorithm for forecasting complex nonlinear dynamical systems. However, classical techniques used for NODE training are ineffective for learning chaotic dynamical systems. In this work, we propose a novel NODE-training approach that allows for robust learning of chaotic dynamical systems. Our method addresses the challenges of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.00568","kind":"arxiv","version":5},"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/2407.00568/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":"2407.00568","created_at":"2026-07-05T09:20:44.880241+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.00568v5","created_at":"2026-07-05T09:20:44.880241+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.00568","created_at":"2026-07-05T09:20:44.880241+00:00"},{"alias_kind":"pith_short_12","alias_value":"UVQHUYJ6L5XQ","created_at":"2026-07-05T09:20:44.880241+00:00"},{"alias_kind":"pith_short_16","alias_value":"UVQHUYJ6L5XQOSR6","created_at":"2026-07-05T09:20:44.880241+00:00"},{"alias_kind":"pith_short_8","alias_value":"UVQHUYJ6","created_at":"2026-07-05T09:20:44.880241+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/UVQHUYJ6L5XQOSR6PTZWOCHCQS","json":"https://pith.science/pith/UVQHUYJ6L5XQOSR6PTZWOCHCQS.json","graph_json":"https://pith.science/api/pith-number/UVQHUYJ6L5XQOSR6PTZWOCHCQS/graph.json","events_json":"https://pith.science/api/pith-number/UVQHUYJ6L5XQOSR6PTZWOCHCQS/events.json","paper":"https://pith.science/paper/UVQHUYJ6"},"agent_actions":{"view_html":"https://pith.science/pith/UVQHUYJ6L5XQOSR6PTZWOCHCQS","download_json":"https://pith.science/pith/UVQHUYJ6L5XQOSR6PTZWOCHCQS.json","view_paper":"https://pith.science/paper/UVQHUYJ6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.00568&json=true","fetch_graph":"https://pith.science/api/pith-number/UVQHUYJ6L5XQOSR6PTZWOCHCQS/graph.json","fetch_events":"https://pith.science/api/pith-number/UVQHUYJ6L5XQOSR6PTZWOCHCQS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UVQHUYJ6L5XQOSR6PTZWOCHCQS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UVQHUYJ6L5XQOSR6PTZWOCHCQS/action/storage_attestation","attest_author":"https://pith.science/pith/UVQHUYJ6L5XQOSR6PTZWOCHCQS/action/author_attestation","sign_citation":"https://pith.science/pith/UVQHUYJ6L5XQOSR6PTZWOCHCQS/action/citation_signature","submit_replication":"https://pith.science/pith/UVQHUYJ6L5XQOSR6PTZWOCHCQS/action/replication_record"}},"created_at":"2026-07-05T09:20:44.880241+00:00","updated_at":"2026-07-05T09:20:44.880241+00:00"}