{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:G74ARBNPEG63VEVBT6ZZSS7BZY","short_pith_number":"pith:G74ARBNP","schema_version":"1.0","canonical_sha256":"37f80885af21bdba92a19fb3994be1ce162fa0263ea73fd7de048b7033c1f967","source":{"kind":"arxiv","id":"2501.04094","version":1},"attestation_state":"computed","paper":{"title":"Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.HE","physics.flu-dyn","physics.plasm-ph"],"primary_cat":"physics.comp-ph","authors_text":"Dimitrios Psaltis, Kaushik Satapathy, Matthew Golden","submitted_at":"2025-01-07T19:00:20Z","abstract_excerpt":"The discovery of dynamical models from data represents a crucial step in advancing our understanding of physical systems. Library-based sparse regression has emerged as a powerful method for inferring governing equations directly from spatiotemporal data, but current model-agnostic implementations remain computationally expensive, limiting their applicability to data that lack substantial complexity. To overcome these challenges, we introduce a scalable framework that enables efficient discovery of complex dynamical models across a wide range of applications. We demonstrate the capabilities 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":"2501.04094","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.comp-ph","submitted_at":"2025-01-07T19:00:20Z","cross_cats_sorted":["astro-ph.HE","physics.flu-dyn","physics.plasm-ph"],"title_canon_sha256":"0c0d448f88a85ad7401f8a0c20cbf3e677999cc538997b0e8b7dc8f547ed3886","abstract_canon_sha256":"b9db0e291240faf9de5bfcf6a920ec40d9d9e299e73e1b9e85d2e19cb205c847"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:58:30.044833Z","signature_b64":"7IIZRIpznHEcEzCbWpNoeBa5loxpXxyOZyxf6bNvDgWpe6hE6LmtRXIVyoXVOrXn4i+fu/oa9F/kw0pb+DQkAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"37f80885af21bdba92a19fb3994be1ce162fa0263ea73fd7de048b7033c1f967","last_reissued_at":"2026-07-05T09:58:30.044385Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:58:30.044385Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.HE","physics.flu-dyn","physics.plasm-ph"],"primary_cat":"physics.comp-ph","authors_text":"Dimitrios Psaltis, Kaushik Satapathy, Matthew Golden","submitted_at":"2025-01-07T19:00:20Z","abstract_excerpt":"The discovery of dynamical models from data represents a crucial step in advancing our understanding of physical systems. Library-based sparse regression has emerged as a powerful method for inferring governing equations directly from spatiotemporal data, but current model-agnostic implementations remain computationally expensive, limiting their applicability to data that lack substantial complexity. To overcome these challenges, we introduce a scalable framework that enables efficient discovery of complex dynamical models across a wide range of applications. We demonstrate the capabilities of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.04094","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/2501.04094/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":"2501.04094","created_at":"2026-07-05T09:58:30.044443+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.04094v1","created_at":"2026-07-05T09:58:30.044443+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.04094","created_at":"2026-07-05T09:58:30.044443+00:00"},{"alias_kind":"pith_short_12","alias_value":"G74ARBNPEG63","created_at":"2026-07-05T09:58:30.044443+00:00"},{"alias_kind":"pith_short_16","alias_value":"G74ARBNPEG63VEVB","created_at":"2026-07-05T09:58:30.044443+00:00"},{"alias_kind":"pith_short_8","alias_value":"G74ARBNP","created_at":"2026-07-05T09:58:30.044443+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.18414","citing_title":"Balance-Guided Sparse Identification of Multiscale Nonlinear PDEs with Small-coefficient Terms","ref_index":36,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/G74ARBNPEG63VEVBT6ZZSS7BZY","json":"https://pith.science/pith/G74ARBNPEG63VEVBT6ZZSS7BZY.json","graph_json":"https://pith.science/api/pith-number/G74ARBNPEG63VEVBT6ZZSS7BZY/graph.json","events_json":"https://pith.science/api/pith-number/G74ARBNPEG63VEVBT6ZZSS7BZY/events.json","paper":"https://pith.science/paper/G74ARBNP"},"agent_actions":{"view_html":"https://pith.science/pith/G74ARBNPEG63VEVBT6ZZSS7BZY","download_json":"https://pith.science/pith/G74ARBNPEG63VEVBT6ZZSS7BZY.json","view_paper":"https://pith.science/paper/G74ARBNP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.04094&json=true","fetch_graph":"https://pith.science/api/pith-number/G74ARBNPEG63VEVBT6ZZSS7BZY/graph.json","fetch_events":"https://pith.science/api/pith-number/G74ARBNPEG63VEVBT6ZZSS7BZY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G74ARBNPEG63VEVBT6ZZSS7BZY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G74ARBNPEG63VEVBT6ZZSS7BZY/action/storage_attestation","attest_author":"https://pith.science/pith/G74ARBNPEG63VEVBT6ZZSS7BZY/action/author_attestation","sign_citation":"https://pith.science/pith/G74ARBNPEG63VEVBT6ZZSS7BZY/action/citation_signature","submit_replication":"https://pith.science/pith/G74ARBNPEG63VEVBT6ZZSS7BZY/action/replication_record"}},"created_at":"2026-07-05T09:58:30.044443+00:00","updated_at":"2026-07-05T09:58:30.044443+00:00"}