{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:OJCPHZ3YXLXSM72BFKVVCBBEMN","short_pith_number":"pith:OJCPHZ3Y","schema_version":"1.0","canonical_sha256":"7244f3e778baef267f412aab510424636f30dbd40991c2ea5d98e9f20cd4bf84","source":{"kind":"arxiv","id":"2404.10024","version":1},"attestation_state":"computed","paper":{"title":"ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.ET","cs.LG","physics.ao-ph"],"primary_cat":"cs.AI","authors_text":"Markus Heinonen, Vikas Garg, Yogesh Verma","submitted_at":"2024-04-15T06:38:21Z","abstract_excerpt":"Climate and weather prediction traditionally relies on complex numerical simulations of atmospheric physics. Deep learning approaches, such as transformers, have recently challenged the simulation paradigm with complex network forecasts. However, they often act as data-driven black-box models that neglect the underlying physics and lack uncertainty quantification. We address these limitations with ClimODE, a spatiotemporal continuous-time process that implements a key principle of advection from statistical mechanics, namely, weather changes due to a spatial movement of quantities over time. C"},"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":"2404.10024","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-04-15T06:38:21Z","cross_cats_sorted":["cs.ET","cs.LG","physics.ao-ph"],"title_canon_sha256":"9f5107629852b06d98b0bbfb476007a9302368fc0dcf76ddfdf7a5aa13d06461","abstract_canon_sha256":"6039c43b8057b3fdd5891030343af71585081b822acd8c14adcc305b10ffe72a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:08:23.932598Z","signature_b64":"V3hxhH8Mimlz8ufnrf2YqC7J5HyB6qUcIHhBjROi7fkwvGB4rzZGtR2LtmWquh78C2pWlF9kP4V3yCnRbCRFDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7244f3e778baef267f412aab510424636f30dbd40991c2ea5d98e9f20cd4bf84","last_reissued_at":"2026-07-05T08:08:23.932134Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:08:23.932134Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.ET","cs.LG","physics.ao-ph"],"primary_cat":"cs.AI","authors_text":"Markus Heinonen, Vikas Garg, Yogesh Verma","submitted_at":"2024-04-15T06:38:21Z","abstract_excerpt":"Climate and weather prediction traditionally relies on complex numerical simulations of atmospheric physics. Deep learning approaches, such as transformers, have recently challenged the simulation paradigm with complex network forecasts. However, they often act as data-driven black-box models that neglect the underlying physics and lack uncertainty quantification. We address these limitations with ClimODE, a spatiotemporal continuous-time process that implements a key principle of advection from statistical mechanics, namely, weather changes due to a spatial movement of quantities over time. C"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.10024","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/2404.10024/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":"2404.10024","created_at":"2026-07-05T08:08:23.932191+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.10024v1","created_at":"2026-07-05T08:08:23.932191+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.10024","created_at":"2026-07-05T08:08:23.932191+00:00"},{"alias_kind":"pith_short_12","alias_value":"OJCPHZ3YXLXS","created_at":"2026-07-05T08:08:23.932191+00:00"},{"alias_kind":"pith_short_16","alias_value":"OJCPHZ3YXLXSM72B","created_at":"2026-07-05T08:08:23.932191+00:00"},{"alias_kind":"pith_short_8","alias_value":"OJCPHZ3Y","created_at":"2026-07-05T08:08:23.932191+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.10642","citing_title":"PhysMetrics.Weather: An Evaluation Framework for Physical Consistency in ML Weather Models","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2605.31204","citing_title":"Probabilistic Precipitation Nowcasting with Rectified Flow Transformers","ref_index":141,"is_internal_anchor":false},{"citing_arxiv_id":"2410.06074","citing_title":"Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2601.14044","citing_title":"Weather-R1: Logically Consistent Reinforcement Fine-Tuning for Multimodal Reasoning in Meteorology","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07522","citing_title":"WeatherSyn: An Instruction Tuning MLLM For Weather Forecasting Report Generation","ref_index":65,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OJCPHZ3YXLXSM72BFKVVCBBEMN","json":"https://pith.science/pith/OJCPHZ3YXLXSM72BFKVVCBBEMN.json","graph_json":"https://pith.science/api/pith-number/OJCPHZ3YXLXSM72BFKVVCBBEMN/graph.json","events_json":"https://pith.science/api/pith-number/OJCPHZ3YXLXSM72BFKVVCBBEMN/events.json","paper":"https://pith.science/paper/OJCPHZ3Y"},"agent_actions":{"view_html":"https://pith.science/pith/OJCPHZ3YXLXSM72BFKVVCBBEMN","download_json":"https://pith.science/pith/OJCPHZ3YXLXSM72BFKVVCBBEMN.json","view_paper":"https://pith.science/paper/OJCPHZ3Y","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.10024&json=true","fetch_graph":"https://pith.science/api/pith-number/OJCPHZ3YXLXSM72BFKVVCBBEMN/graph.json","fetch_events":"https://pith.science/api/pith-number/OJCPHZ3YXLXSM72BFKVVCBBEMN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OJCPHZ3YXLXSM72BFKVVCBBEMN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OJCPHZ3YXLXSM72BFKVVCBBEMN/action/storage_attestation","attest_author":"https://pith.science/pith/OJCPHZ3YXLXSM72BFKVVCBBEMN/action/author_attestation","sign_citation":"https://pith.science/pith/OJCPHZ3YXLXSM72BFKVVCBBEMN/action/citation_signature","submit_replication":"https://pith.science/pith/OJCPHZ3YXLXSM72BFKVVCBBEMN/action/replication_record"}},"created_at":"2026-07-05T08:08:23.932191+00:00","updated_at":"2026-07-05T08:08:23.932191+00:00"}