{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:DISU56VUQEDBDCREJOFFEHWDA4","short_pith_number":"pith:DISU56VU","schema_version":"1.0","canonical_sha256":"1a254efab48106118a244b8a521ec307278179e1ca24312b7d0ae73024bdd7a0","source":{"kind":"arxiv","id":"2502.04703","version":1},"attestation_state":"computed","paper":{"title":"Symbolic Regression of Data-Driven Reduced Order Model Closures for Under-Resolved, Convection-Dominated Flows","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.NA","physics.flu-dyn"],"primary_cat":"math.NA","authors_text":"Alessandro Lucantonio, Ping-Hsuan Tsai, Simone Manti, Traian Iliescu","submitted_at":"2025-02-07T07:14:41Z","abstract_excerpt":"Data-driven closures correct the standard reduced order models (ROMs) to increase their accuracy in under-resolved, convection-dominated flows. There are two types of data-driven ROM closures in current use: (i) structural, with simple ansatzes (e.g., linear or quadratic); and (ii) machine learning-based, with neural network ansatzes. We propose a novel symbolic regression (SR) data-driven ROM closure strategy, which combines the advantages of current approaches and eliminates their drawbacks. As a result, the new data-driven SR closures yield ROMs that are interpretable, parsimonious, accurat"},"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":"2502.04703","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.NA","submitted_at":"2025-02-07T07:14:41Z","cross_cats_sorted":["cs.LG","cs.NA","physics.flu-dyn"],"title_canon_sha256":"ad80c36510627e993876ec0c6288a0f583c732179e89e3bbde91c8efd9389857","abstract_canon_sha256":"59263fa4276943d5cc9cda5410fbad235d5cc54b11af84d4143f8c9b9a560aae"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:10:55.565810Z","signature_b64":"l+ALGjLXmEKNRSkE42A/cIcxOWemWCvrlRngFm3xuq+y01Tnrx+VLVRo5J7nPIAWLmpxgyZ8195wKnPBE6cvCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1a254efab48106118a244b8a521ec307278179e1ca24312b7d0ae73024bdd7a0","last_reissued_at":"2026-07-05T10:10:55.565277Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:10:55.565277Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Symbolic Regression of Data-Driven Reduced Order Model Closures for Under-Resolved, Convection-Dominated Flows","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.NA","physics.flu-dyn"],"primary_cat":"math.NA","authors_text":"Alessandro Lucantonio, Ping-Hsuan Tsai, Simone Manti, Traian Iliescu","submitted_at":"2025-02-07T07:14:41Z","abstract_excerpt":"Data-driven closures correct the standard reduced order models (ROMs) to increase their accuracy in under-resolved, convection-dominated flows. There are two types of data-driven ROM closures in current use: (i) structural, with simple ansatzes (e.g., linear or quadratic); and (ii) machine learning-based, with neural network ansatzes. We propose a novel symbolic regression (SR) data-driven ROM closure strategy, which combines the advantages of current approaches and eliminates their drawbacks. As a result, the new data-driven SR closures yield ROMs that are interpretable, parsimonious, accurat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.04703","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/2502.04703/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":"2502.04703","created_at":"2026-07-05T10:10:55.565361+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.04703v1","created_at":"2026-07-05T10:10:55.565361+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.04703","created_at":"2026-07-05T10:10:55.565361+00:00"},{"alias_kind":"pith_short_12","alias_value":"DISU56VUQEDB","created_at":"2026-07-05T10:10:55.565361+00:00"},{"alias_kind":"pith_short_16","alias_value":"DISU56VUQEDBDCRE","created_at":"2026-07-05T10:10:55.565361+00:00"},{"alias_kind":"pith_short_8","alias_value":"DISU56VU","created_at":"2026-07-05T10:10:55.565361+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.18310","citing_title":"Verifiability and Limit Consistency of Eddy Viscosity Large Eddy Simulation Reduced Order Models","ref_index":34,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DISU56VUQEDBDCREJOFFEHWDA4","json":"https://pith.science/pith/DISU56VUQEDBDCREJOFFEHWDA4.json","graph_json":"https://pith.science/api/pith-number/DISU56VUQEDBDCREJOFFEHWDA4/graph.json","events_json":"https://pith.science/api/pith-number/DISU56VUQEDBDCREJOFFEHWDA4/events.json","paper":"https://pith.science/paper/DISU56VU"},"agent_actions":{"view_html":"https://pith.science/pith/DISU56VUQEDBDCREJOFFEHWDA4","download_json":"https://pith.science/pith/DISU56VUQEDBDCREJOFFEHWDA4.json","view_paper":"https://pith.science/paper/DISU56VU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.04703&json=true","fetch_graph":"https://pith.science/api/pith-number/DISU56VUQEDBDCREJOFFEHWDA4/graph.json","fetch_events":"https://pith.science/api/pith-number/DISU56VUQEDBDCREJOFFEHWDA4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DISU56VUQEDBDCREJOFFEHWDA4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DISU56VUQEDBDCREJOFFEHWDA4/action/storage_attestation","attest_author":"https://pith.science/pith/DISU56VUQEDBDCREJOFFEHWDA4/action/author_attestation","sign_citation":"https://pith.science/pith/DISU56VUQEDBDCREJOFFEHWDA4/action/citation_signature","submit_replication":"https://pith.science/pith/DISU56VUQEDBDCREJOFFEHWDA4/action/replication_record"}},"created_at":"2026-07-05T10:10:55.565361+00:00","updated_at":"2026-07-05T10:10:55.565361+00:00"}