{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:F4VDJEFZPDRMPW24QOBXF72VIQ","short_pith_number":"pith:F4VDJEFZ","schema_version":"1.0","canonical_sha256":"2f2a3490b978e2c7db5c838372ff55440e4e7a89c948fcc5eb95a059a58bc2f1","source":{"kind":"arxiv","id":"1803.04779","version":1},"attestation_state":"computed","paper":{"title":"Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["nlin.CD","stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexander Wikner, Brian Hunt, Edward Ott, Jaideep Pathak, Michelle Girvan, Rebeckah Fussell, Sarthak Chandra","submitted_at":"2018-03-09T21:02:25Z","abstract_excerpt":"A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the physical processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-base"},"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":"1803.04779","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-09T21:02:25Z","cross_cats_sorted":["nlin.CD","stat.ML"],"title_canon_sha256":"ce6df985733b7aa79790cc6ffab136dde9bfc36ff8ab77cff44114ff4b9604c2","abstract_canon_sha256":"d62d302e6d0e88368b6d56eed44da9c4a917524a22986e0efd7a996c10839a14"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:16:25.287972Z","signature_b64":"aDtyTb8gLfoYP1g+VrPYSqKpW3xWg6OuOnPI259Z7homfqKykwaFvac6aI+l1mFxq8YZ0XZv9++mw2NqpoztBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2f2a3490b978e2c7db5c838372ff55440e4e7a89c948fcc5eb95a059a58bc2f1","last_reissued_at":"2026-05-18T00:16:25.287381Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:16:25.287381Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["nlin.CD","stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexander Wikner, Brian Hunt, Edward Ott, Jaideep Pathak, Michelle Girvan, Rebeckah Fussell, Sarthak Chandra","submitted_at":"2018-03-09T21:02:25Z","abstract_excerpt":"A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the physical processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-base"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.04779","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":""},"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":"1803.04779","created_at":"2026-05-18T00:16:25.287463+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.04779v1","created_at":"2026-05-18T00:16:25.287463+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.04779","created_at":"2026-05-18T00:16:25.287463+00:00"},{"alias_kind":"pith_short_12","alias_value":"F4VDJEFZPDRM","created_at":"2026-05-18T12:32:22.470017+00:00"},{"alias_kind":"pith_short_16","alias_value":"F4VDJEFZPDRMPW24","created_at":"2026-05-18T12:32:22.470017+00:00"},{"alias_kind":"pith_short_8","alias_value":"F4VDJEFZ","created_at":"2026-05-18T12:32:22.470017+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/F4VDJEFZPDRMPW24QOBXF72VIQ","json":"https://pith.science/pith/F4VDJEFZPDRMPW24QOBXF72VIQ.json","graph_json":"https://pith.science/api/pith-number/F4VDJEFZPDRMPW24QOBXF72VIQ/graph.json","events_json":"https://pith.science/api/pith-number/F4VDJEFZPDRMPW24QOBXF72VIQ/events.json","paper":"https://pith.science/paper/F4VDJEFZ"},"agent_actions":{"view_html":"https://pith.science/pith/F4VDJEFZPDRMPW24QOBXF72VIQ","download_json":"https://pith.science/pith/F4VDJEFZPDRMPW24QOBXF72VIQ.json","view_paper":"https://pith.science/paper/F4VDJEFZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.04779&json=true","fetch_graph":"https://pith.science/api/pith-number/F4VDJEFZPDRMPW24QOBXF72VIQ/graph.json","fetch_events":"https://pith.science/api/pith-number/F4VDJEFZPDRMPW24QOBXF72VIQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/F4VDJEFZPDRMPW24QOBXF72VIQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/F4VDJEFZPDRMPW24QOBXF72VIQ/action/storage_attestation","attest_author":"https://pith.science/pith/F4VDJEFZPDRMPW24QOBXF72VIQ/action/author_attestation","sign_citation":"https://pith.science/pith/F4VDJEFZPDRMPW24QOBXF72VIQ/action/citation_signature","submit_replication":"https://pith.science/pith/F4VDJEFZPDRMPW24QOBXF72VIQ/action/replication_record"}},"created_at":"2026-05-18T00:16:25.287463+00:00","updated_at":"2026-05-18T00:16:25.287463+00:00"}