{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:WDIZ7VHFBA6Q6J4SPRMEOIQGJC","short_pith_number":"pith:WDIZ7VHF","schema_version":"1.0","canonical_sha256":"b0d19fd4e5083d0f27927c5847220648b6eb0414b22a3b9c1c7fd8b9f9a2a0c4","source":{"kind":"arxiv","id":"2410.05572","version":1},"attestation_state":"computed","paper":{"title":"Improved deep learning of chaotic dynamical systems with multistep penalty losses","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","math.DS"],"primary_cat":"cs.LG","authors_text":"Ashesh Chattopadhyay, Dibyajyoti Chakraborty, Romit Maulik, Seung Whan Chung","submitted_at":"2024-10-08T00:13:57Z","abstract_excerpt":"Predicting the long-term behavior of chaotic systems remains a formidable challenge due to their extreme sensitivity to initial conditions and the inherent limitations of traditional data-driven modeling approaches. This paper introduces a novel framework that addresses these challenges by leveraging the recently proposed multi-step penalty (MP) optimization technique. Our approach extends the applicability of MP optimization to a wide range of deep learning architectures, including Fourier Neural Operators and UNETs. By introducing penalized local discontinuities in the forecast trajectory, w"},"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":"2410.05572","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-08T00:13:57Z","cross_cats_sorted":["cs.AI","math.DS"],"title_canon_sha256":"7d371936043eca12eadb54415f80db8ef039845a15935ad0a4b1bc10d335e9ac","abstract_canon_sha256":"c7f38b70961cecb619c617fc468927a422249f8100e8d1ed7d14e6ed3b4b00fd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:17:21.126323Z","signature_b64":"r8lC+T1Y6qiHcBVmGmFPdx6k8dvBSPMHS4sInEFKa++BPI5vmaa4wspAExxSBDSFNNFsq0Ic5FYhrn+KTtnNAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b0d19fd4e5083d0f27927c5847220648b6eb0414b22a3b9c1c7fd8b9f9a2a0c4","last_reissued_at":"2026-07-05T09:17:21.125819Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:17:21.125819Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improved deep learning of chaotic dynamical systems with multistep penalty losses","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","math.DS"],"primary_cat":"cs.LG","authors_text":"Ashesh Chattopadhyay, Dibyajyoti Chakraborty, Romit Maulik, Seung Whan Chung","submitted_at":"2024-10-08T00:13:57Z","abstract_excerpt":"Predicting the long-term behavior of chaotic systems remains a formidable challenge due to their extreme sensitivity to initial conditions and the inherent limitations of traditional data-driven modeling approaches. This paper introduces a novel framework that addresses these challenges by leveraging the recently proposed multi-step penalty (MP) optimization technique. Our approach extends the applicability of MP optimization to a wide range of deep learning architectures, including Fourier Neural Operators and UNETs. By introducing penalized local discontinuities in the forecast trajectory, w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.05572","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/2410.05572/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":"2410.05572","created_at":"2026-07-05T09:17:21.125881+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.05572v1","created_at":"2026-07-05T09:17:21.125881+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.05572","created_at":"2026-07-05T09:17:21.125881+00:00"},{"alias_kind":"pith_short_12","alias_value":"WDIZ7VHFBA6Q","created_at":"2026-07-05T09:17:21.125881+00:00"},{"alias_kind":"pith_short_16","alias_value":"WDIZ7VHFBA6Q6J4S","created_at":"2026-07-05T09:17:21.125881+00:00"},{"alias_kind":"pith_short_8","alias_value":"WDIZ7VHF","created_at":"2026-07-05T09:17:21.125881+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2511.06609","citing_title":"A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series","ref_index":35,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WDIZ7VHFBA6Q6J4SPRMEOIQGJC","json":"https://pith.science/pith/WDIZ7VHFBA6Q6J4SPRMEOIQGJC.json","graph_json":"https://pith.science/api/pith-number/WDIZ7VHFBA6Q6J4SPRMEOIQGJC/graph.json","events_json":"https://pith.science/api/pith-number/WDIZ7VHFBA6Q6J4SPRMEOIQGJC/events.json","paper":"https://pith.science/paper/WDIZ7VHF"},"agent_actions":{"view_html":"https://pith.science/pith/WDIZ7VHFBA6Q6J4SPRMEOIQGJC","download_json":"https://pith.science/pith/WDIZ7VHFBA6Q6J4SPRMEOIQGJC.json","view_paper":"https://pith.science/paper/WDIZ7VHF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.05572&json=true","fetch_graph":"https://pith.science/api/pith-number/WDIZ7VHFBA6Q6J4SPRMEOIQGJC/graph.json","fetch_events":"https://pith.science/api/pith-number/WDIZ7VHFBA6Q6J4SPRMEOIQGJC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WDIZ7VHFBA6Q6J4SPRMEOIQGJC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WDIZ7VHFBA6Q6J4SPRMEOIQGJC/action/storage_attestation","attest_author":"https://pith.science/pith/WDIZ7VHFBA6Q6J4SPRMEOIQGJC/action/author_attestation","sign_citation":"https://pith.science/pith/WDIZ7VHFBA6Q6J4SPRMEOIQGJC/action/citation_signature","submit_replication":"https://pith.science/pith/WDIZ7VHFBA6Q6J4SPRMEOIQGJC/action/replication_record"}},"created_at":"2026-07-05T09:17:21.125881+00:00","updated_at":"2026-07-05T09:17:21.125881+00:00"}