{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:PS6RRMF7NDOEU46BJLILOPB5XN","short_pith_number":"pith:PS6RRMF7","schema_version":"1.0","canonical_sha256":"7cbd18b0bf68dc4a73c14ad0b73c3dbb4316f03a0dc6d5b7b8c2aabdfb7331cc","source":{"kind":"arxiv","id":"1905.07027","version":2},"attestation_state":"computed","paper":{"title":"Reduced-order modeling using Dynamic Mode Decomposition and Least Angle Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Francis D. Lagor, John Graff, Tarunraj Singh, Xianzhang Xu","submitted_at":"2019-05-16T20:35:09Z","abstract_excerpt":"Dynamic Mode Decomposition (DMD) yields a linear, approximate model of a system's dynamics that is built from data. We seek to reduce the order of this model by identifying a reduced set of modes that best fit the output. We adopt a model selection algorithm from statistics and machine learning known as Least Angle Regression (LARS). We modify LARS to be complex-valued and utilize LARS to select DMD modes. We refer to the resulting algorithm as Least Angle Regression for Dynamic Mode Decomposition (LARS4DMD). Sparsity-Promoting Dynamic Mode Decomposition (DMDSP), a popular mode-selection algor"},"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":"1905.07027","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-05-16T20:35:09Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"98dfc9ce89da9b91d6138375ae312781bac9d68ce5b87f6bc5da412bff64c82c","abstract_canon_sha256":"ac3eac4567c93e201eb7037fa8dfe36457865186bd5ec018e3c5d4e8d9136dd0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:34:00.743974Z","signature_b64":"ypNGlEDXeP/dkUM6eA2d2qtzwNL5SjgTwu4ZC7EhWW/vdnmshXS+jvzdgTbLbiUgwz20TItPYPM3u+3K+MsFAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7cbd18b0bf68dc4a73c14ad0b73c3dbb4316f03a0dc6d5b7b8c2aabdfb7331cc","last_reissued_at":"2026-07-05T00:34:00.743606Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:34:00.743606Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Reduced-order modeling using Dynamic Mode Decomposition and Least Angle Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Francis D. Lagor, John Graff, Tarunraj Singh, Xianzhang Xu","submitted_at":"2019-05-16T20:35:09Z","abstract_excerpt":"Dynamic Mode Decomposition (DMD) yields a linear, approximate model of a system's dynamics that is built from data. We seek to reduce the order of this model by identifying a reduced set of modes that best fit the output. We adopt a model selection algorithm from statistics and machine learning known as Least Angle Regression (LARS). We modify LARS to be complex-valued and utilize LARS to select DMD modes. We refer to the resulting algorithm as Least Angle Regression for Dynamic Mode Decomposition (LARS4DMD). Sparsity-Promoting Dynamic Mode Decomposition (DMDSP), a popular mode-selection algor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.07027","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/1905.07027/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":"1905.07027","created_at":"2026-07-05T00:34:00.743669+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.07027v2","created_at":"2026-07-05T00:34:00.743669+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.07027","created_at":"2026-07-05T00:34:00.743669+00:00"},{"alias_kind":"pith_short_12","alias_value":"PS6RRMF7NDOE","created_at":"2026-07-05T00:34:00.743669+00:00"},{"alias_kind":"pith_short_16","alias_value":"PS6RRMF7NDOEU46B","created_at":"2026-07-05T00:34:00.743669+00:00"},{"alias_kind":"pith_short_8","alias_value":"PS6RRMF7","created_at":"2026-07-05T00:34:00.743669+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/PS6RRMF7NDOEU46BJLILOPB5XN","json":"https://pith.science/pith/PS6RRMF7NDOEU46BJLILOPB5XN.json","graph_json":"https://pith.science/api/pith-number/PS6RRMF7NDOEU46BJLILOPB5XN/graph.json","events_json":"https://pith.science/api/pith-number/PS6RRMF7NDOEU46BJLILOPB5XN/events.json","paper":"https://pith.science/paper/PS6RRMF7"},"agent_actions":{"view_html":"https://pith.science/pith/PS6RRMF7NDOEU46BJLILOPB5XN","download_json":"https://pith.science/pith/PS6RRMF7NDOEU46BJLILOPB5XN.json","view_paper":"https://pith.science/paper/PS6RRMF7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.07027&json=true","fetch_graph":"https://pith.science/api/pith-number/PS6RRMF7NDOEU46BJLILOPB5XN/graph.json","fetch_events":"https://pith.science/api/pith-number/PS6RRMF7NDOEU46BJLILOPB5XN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PS6RRMF7NDOEU46BJLILOPB5XN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PS6RRMF7NDOEU46BJLILOPB5XN/action/storage_attestation","attest_author":"https://pith.science/pith/PS6RRMF7NDOEU46BJLILOPB5XN/action/author_attestation","sign_citation":"https://pith.science/pith/PS6RRMF7NDOEU46BJLILOPB5XN/action/citation_signature","submit_replication":"https://pith.science/pith/PS6RRMF7NDOEU46BJLILOPB5XN/action/replication_record"}},"created_at":"2026-07-05T00:34:00.743669+00:00","updated_at":"2026-07-05T00:34:00.743669+00:00"}