{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:L47YX6EWXPHBNFAOG7GZ3PJACD","short_pith_number":"pith:L47YX6EW","schema_version":"1.0","canonical_sha256":"5f3f8bf896bbce16940e37cd9dbd2010f225ed90b9f9ddb50328290a220d2347","source":{"kind":"arxiv","id":"2501.00738","version":2},"attestation_state":"computed","paper":{"title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","physics.comp-ph"],"primary_cat":"physics.geo-ph","authors_text":"Daniel A. Messenger, David M. Bortz, Seth Minor, Vanja Dukic","submitted_at":"2025-01-01T06:03:07Z","abstract_excerpt":"The multiscale and turbulent nature of Earth's atmosphere has historically rendered accurate weather modeling a hard problem. Recently, there has been an explosion of interest surrounding data-driven approaches to weather modeling, which in many cases show improved forecasting accuracy and computational efficiency when compared to traditional methods. However, many of the current data-driven approaches employ highly parameterized neural networks, often resulting in uninterpretable models and limited gains in scientific understanding. In this work, we address the interpretability problem by exp"},"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":"2501.00738","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"physics.geo-ph","submitted_at":"2025-01-01T06:03:07Z","cross_cats_sorted":["cs.LG","physics.comp-ph"],"title_canon_sha256":"3a56c69005fe28089c6d8d821673d39c39d5a3be12b7f8696fd6c36cf1759c56","abstract_canon_sha256":"3f7cd29cf3f017269c252e9bc300503100e545fc2e66a95c15276be25383b2ef"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:32:22.670902Z","signature_b64":"m6LUSFCTGRRyqckT6nRUF3Js1jE7H6TLo24GK8R7QBDMhj25IaFawaiTKZNARc8AsXcYgv8APK2TCwgn7StzCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5f3f8bf896bbce16940e37cd9dbd2010f225ed90b9f9ddb50328290a220d2347","last_reissued_at":"2026-07-05T11:32:22.670406Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:32:22.670406Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","physics.comp-ph"],"primary_cat":"physics.geo-ph","authors_text":"Daniel A. Messenger, David M. Bortz, Seth Minor, Vanja Dukic","submitted_at":"2025-01-01T06:03:07Z","abstract_excerpt":"The multiscale and turbulent nature of Earth's atmosphere has historically rendered accurate weather modeling a hard problem. Recently, there has been an explosion of interest surrounding data-driven approaches to weather modeling, which in many cases show improved forecasting accuracy and computational efficiency when compared to traditional methods. However, many of the current data-driven approaches employ highly parameterized neural networks, often resulting in uninterpretable models and limited gains in scientific understanding. In this work, we address the interpretability problem by exp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.00738","kind":"arxiv","version":2},"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/2501.00738/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":"2501.00738","created_at":"2026-07-05T11:32:22.670465+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.00738v2","created_at":"2026-07-05T11:32:22.670465+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.00738","created_at":"2026-07-05T11:32:22.670465+00:00"},{"alias_kind":"pith_short_12","alias_value":"L47YX6EWXPHB","created_at":"2026-07-05T11:32:22.670465+00:00"},{"alias_kind":"pith_short_16","alias_value":"L47YX6EWXPHBNFAO","created_at":"2026-07-05T11:32:22.670465+00:00"},{"alias_kind":"pith_short_8","alias_value":"L47YX6EW","created_at":"2026-07-05T11:32:22.670465+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.03206","citing_title":"Weak Form Scientific Machine Learning: Test Function Construction for System Identification","ref_index":29,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L47YX6EWXPHBNFAOG7GZ3PJACD","json":"https://pith.science/pith/L47YX6EWXPHBNFAOG7GZ3PJACD.json","graph_json":"https://pith.science/api/pith-number/L47YX6EWXPHBNFAOG7GZ3PJACD/graph.json","events_json":"https://pith.science/api/pith-number/L47YX6EWXPHBNFAOG7GZ3PJACD/events.json","paper":"https://pith.science/paper/L47YX6EW"},"agent_actions":{"view_html":"https://pith.science/pith/L47YX6EWXPHBNFAOG7GZ3PJACD","download_json":"https://pith.science/pith/L47YX6EWXPHBNFAOG7GZ3PJACD.json","view_paper":"https://pith.science/paper/L47YX6EW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.00738&json=true","fetch_graph":"https://pith.science/api/pith-number/L47YX6EWXPHBNFAOG7GZ3PJACD/graph.json","fetch_events":"https://pith.science/api/pith-number/L47YX6EWXPHBNFAOG7GZ3PJACD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L47YX6EWXPHBNFAOG7GZ3PJACD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L47YX6EWXPHBNFAOG7GZ3PJACD/action/storage_attestation","attest_author":"https://pith.science/pith/L47YX6EWXPHBNFAOG7GZ3PJACD/action/author_attestation","sign_citation":"https://pith.science/pith/L47YX6EWXPHBNFAOG7GZ3PJACD/action/citation_signature","submit_replication":"https://pith.science/pith/L47YX6EWXPHBNFAOG7GZ3PJACD/action/replication_record"}},"created_at":"2026-07-05T11:32:22.670465+00:00","updated_at":"2026-07-05T11:32:22.670465+00:00"}