{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:KPLDFZZ2DGBYRZIGXVEHUUIQRA","short_pith_number":"pith:KPLDFZZ2","schema_version":"1.0","canonical_sha256":"53d632e73a198388e506bd487a511088301ce8a3ca497ce6aca8476ee13a04b9","source":{"kind":"arxiv","id":"2203.11574","version":1},"attestation_state":"computed","paper":{"title":"Higher order dynamic mode decomposition to model reacting flows","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.flu-dyn","authors_text":"Adri\\'an Corrochano, Alessandro Parente, Giuseppe D'Alessio, Soledad Le Clainche","submitted_at":"2022-03-22T09:53:15Z","abstract_excerpt":"In this work, the application of the multi-dimensional higher order dynamic mode decomposition (HODMD) is proposed for the first time to analyse combustion databases. In particular, HODMD has been adapted and combined with other pre-processing techniques (generally used in machine learning), in light of the multivariate nature of the data. A truncation step separate the main dynamics driving the flow from less relevant non-linear dynamics. The method is applied to analyse a database obtained from a Computational Fluid Dynamics (CFD) simulation of an axisymmetric, time varying, non-premixed, co"},"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":"2203.11574","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.flu-dyn","submitted_at":"2022-03-22T09:53:15Z","cross_cats_sorted":[],"title_canon_sha256":"9d9d75ee6e0fbf0ba63ca79ddd77c8e351c41464e674bd5f4cb2978198d6b75d","abstract_canon_sha256":"3e229873c65e1c8be0722e36ba434b4b16412a28691f99dbb64fb12e9c9c4136"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:07:13.533904Z","signature_b64":"VrmIqot0xHO++0LcAbAsAXwOh8eIJNZcVlN8RV9q50cyzUfvAbnj/DRW647enUDWDNQb6kBdHRw2EBN5UFKGDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"53d632e73a198388e506bd487a511088301ce8a3ca497ce6aca8476ee13a04b9","last_reissued_at":"2026-07-05T04:07:13.533488Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:07:13.533488Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Higher order dynamic mode decomposition to model reacting flows","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.flu-dyn","authors_text":"Adri\\'an Corrochano, Alessandro Parente, Giuseppe D'Alessio, Soledad Le Clainche","submitted_at":"2022-03-22T09:53:15Z","abstract_excerpt":"In this work, the application of the multi-dimensional higher order dynamic mode decomposition (HODMD) is proposed for the first time to analyse combustion databases. In particular, HODMD has been adapted and combined with other pre-processing techniques (generally used in machine learning), in light of the multivariate nature of the data. A truncation step separate the main dynamics driving the flow from less relevant non-linear dynamics. The method is applied to analyse a database obtained from a Computational Fluid Dynamics (CFD) simulation of an axisymmetric, time varying, non-premixed, co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.11574","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/2203.11574/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":"2203.11574","created_at":"2026-07-05T04:07:13.533547+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.11574v1","created_at":"2026-07-05T04:07:13.533547+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.11574","created_at":"2026-07-05T04:07:13.533547+00:00"},{"alias_kind":"pith_short_12","alias_value":"KPLDFZZ2DGBY","created_at":"2026-07-05T04:07:13.533547+00:00"},{"alias_kind":"pith_short_16","alias_value":"KPLDFZZ2DGBYRZIG","created_at":"2026-07-05T04:07:13.533547+00:00"},{"alias_kind":"pith_short_8","alias_value":"KPLDFZZ2","created_at":"2026-07-05T04:07:13.533547+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/KPLDFZZ2DGBYRZIGXVEHUUIQRA","json":"https://pith.science/pith/KPLDFZZ2DGBYRZIGXVEHUUIQRA.json","graph_json":"https://pith.science/api/pith-number/KPLDFZZ2DGBYRZIGXVEHUUIQRA/graph.json","events_json":"https://pith.science/api/pith-number/KPLDFZZ2DGBYRZIGXVEHUUIQRA/events.json","paper":"https://pith.science/paper/KPLDFZZ2"},"agent_actions":{"view_html":"https://pith.science/pith/KPLDFZZ2DGBYRZIGXVEHUUIQRA","download_json":"https://pith.science/pith/KPLDFZZ2DGBYRZIGXVEHUUIQRA.json","view_paper":"https://pith.science/paper/KPLDFZZ2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.11574&json=true","fetch_graph":"https://pith.science/api/pith-number/KPLDFZZ2DGBYRZIGXVEHUUIQRA/graph.json","fetch_events":"https://pith.science/api/pith-number/KPLDFZZ2DGBYRZIGXVEHUUIQRA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KPLDFZZ2DGBYRZIGXVEHUUIQRA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KPLDFZZ2DGBYRZIGXVEHUUIQRA/action/storage_attestation","attest_author":"https://pith.science/pith/KPLDFZZ2DGBYRZIGXVEHUUIQRA/action/author_attestation","sign_citation":"https://pith.science/pith/KPLDFZZ2DGBYRZIGXVEHUUIQRA/action/citation_signature","submit_replication":"https://pith.science/pith/KPLDFZZ2DGBYRZIGXVEHUUIQRA/action/replication_record"}},"created_at":"2026-07-05T04:07:13.533547+00:00","updated_at":"2026-07-05T04:07:13.533547+00:00"}