{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:LAGLPNA7ZRZVFOULEPZE7UNS3D","short_pith_number":"pith:LAGLPNA7","schema_version":"1.0","canonical_sha256":"580cb7b41fcc7352ba8b23f24fd1b2d8fe556d3e9d9970820ddbe62d01251db1","source":{"kind":"arxiv","id":"2402.07463","version":1},"attestation_state":"computed","paper":{"title":"PyDMD: A Python package for robust dynamic mode decomposition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY","eess.SY","math.DS","physics.comp-ph"],"primary_cat":"stat.CO","authors_text":"Francesco Andreuzzi, Gianluigi Rozza, J. Nathan Kutz, Karl Lapo, Marco Tezzele, Nicola Demo, Sara M. Ichinaga, Steven L. Brunton","submitted_at":"2024-02-12T07:52:55Z","abstract_excerpt":"The dynamic mode decomposition (DMD) is a simple and powerful data-driven modeling technique that is capable of revealing coherent spatiotemporal patterns from data. The method's linear algebra-based formulation additionally allows for a variety of optimizations and extensions that make the algorithm practical and viable for real-world data analysis. As a result, DMD has grown to become a leading method for dynamical system analysis across multiple scientific disciplines. PyDMD is a Python package that implements DMD and several of its major variants. In this work, we expand the PyDMD package "},"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":"2402.07463","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.CO","submitted_at":"2024-02-12T07:52:55Z","cross_cats_sorted":["cs.SY","eess.SY","math.DS","physics.comp-ph"],"title_canon_sha256":"d279d49a128d9d38ddccebfefda6bdd81e4f5f0261a6b75f6277dd655c308f8b","abstract_canon_sha256":"18dfc79156e48d407f39dca20ab0590cc88e97d0ee04667f19237b25a469b0b8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:44:07.089476Z","signature_b64":"Ev5A9cwzx00rnEcisbO7dn7I1bpuz9gxykJMw+0ZZSJdkFC1qykQXsvCwoO/7ClO2uNcqHUbj/DVNfY2vjmXBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"580cb7b41fcc7352ba8b23f24fd1b2d8fe556d3e9d9970820ddbe62d01251db1","last_reissued_at":"2026-07-05T07:44:07.088975Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:44:07.088975Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PyDMD: A Python package for robust dynamic mode decomposition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY","eess.SY","math.DS","physics.comp-ph"],"primary_cat":"stat.CO","authors_text":"Francesco Andreuzzi, Gianluigi Rozza, J. Nathan Kutz, Karl Lapo, Marco Tezzele, Nicola Demo, Sara M. Ichinaga, Steven L. Brunton","submitted_at":"2024-02-12T07:52:55Z","abstract_excerpt":"The dynamic mode decomposition (DMD) is a simple and powerful data-driven modeling technique that is capable of revealing coherent spatiotemporal patterns from data. The method's linear algebra-based formulation additionally allows for a variety of optimizations and extensions that make the algorithm practical and viable for real-world data analysis. As a result, DMD has grown to become a leading method for dynamical system analysis across multiple scientific disciplines. PyDMD is a Python package that implements DMD and several of its major variants. In this work, we expand the PyDMD package "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.07463","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/2402.07463/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":"2402.07463","created_at":"2026-07-05T07:44:07.089035+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.07463v1","created_at":"2026-07-05T07:44:07.089035+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.07463","created_at":"2026-07-05T07:44:07.089035+00:00"},{"alias_kind":"pith_short_12","alias_value":"LAGLPNA7ZRZV","created_at":"2026-07-05T07:44:07.089035+00:00"},{"alias_kind":"pith_short_16","alias_value":"LAGLPNA7ZRZVFOUL","created_at":"2026-07-05T07:44:07.089035+00:00"},{"alias_kind":"pith_short_8","alias_value":"LAGLPNA7","created_at":"2026-07-05T07:44:07.089035+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.05202","citing_title":"Multi-Fidelity Learning with Shallow Recurrent Decoders for Multi-Physics Applications","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2410.13469","citing_title":"Interpreting Temporal Graph Neural Networks with Koopman Theory","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2510.23166","citing_title":"Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms","ref_index":33,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LAGLPNA7ZRZVFOULEPZE7UNS3D","json":"https://pith.science/pith/LAGLPNA7ZRZVFOULEPZE7UNS3D.json","graph_json":"https://pith.science/api/pith-number/LAGLPNA7ZRZVFOULEPZE7UNS3D/graph.json","events_json":"https://pith.science/api/pith-number/LAGLPNA7ZRZVFOULEPZE7UNS3D/events.json","paper":"https://pith.science/paper/LAGLPNA7"},"agent_actions":{"view_html":"https://pith.science/pith/LAGLPNA7ZRZVFOULEPZE7UNS3D","download_json":"https://pith.science/pith/LAGLPNA7ZRZVFOULEPZE7UNS3D.json","view_paper":"https://pith.science/paper/LAGLPNA7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.07463&json=true","fetch_graph":"https://pith.science/api/pith-number/LAGLPNA7ZRZVFOULEPZE7UNS3D/graph.json","fetch_events":"https://pith.science/api/pith-number/LAGLPNA7ZRZVFOULEPZE7UNS3D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LAGLPNA7ZRZVFOULEPZE7UNS3D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LAGLPNA7ZRZVFOULEPZE7UNS3D/action/storage_attestation","attest_author":"https://pith.science/pith/LAGLPNA7ZRZVFOULEPZE7UNS3D/action/author_attestation","sign_citation":"https://pith.science/pith/LAGLPNA7ZRZVFOULEPZE7UNS3D/action/citation_signature","submit_replication":"https://pith.science/pith/LAGLPNA7ZRZVFOULEPZE7UNS3D/action/replication_record"}},"created_at":"2026-07-05T07:44:07.089035+00:00","updated_at":"2026-07-05T07:44:07.089035+00:00"}