{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:WVY6FWBAHU2NXFT2UIBP5S4XTL","short_pith_number":"pith:WVY6FWBA","schema_version":"1.0","canonical_sha256":"b571e2d8203d34db967aa202fecb979af8e7751421a025041e889312b9765f34","source":{"kind":"arxiv","id":"2411.17433","version":1},"attestation_state":"computed","paper":{"title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"physics.flu-dyn","authors_text":"Ashton Hetherington, Javier L\\'opez Leon\\'es, Soledad Le Clainche","submitted_at":"2024-11-26T13:43:50Z","abstract_excerpt":"This article introduces a novel methodology that integrates singular value decomposition (SVD) with a shallow linear neural network for forecasting high resolution fluid mechanics data. The method, termed LC-SVD-DLinear, combines a low-cost variant of singular value decomposition (LC-SVD) with the DLinear architecture, which decomposes the input features-specifically, the temporal coefficients-into trend and seasonality components, enabling a shallow neural network to capture the non-linear dynamics of the temporal data. This methodology uses under-resolved data, which can either be input dire"},"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":"2411.17433","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.flu-dyn","submitted_at":"2024-11-26T13:43:50Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7db670855333bc25e439ad58eff313727b061525b63f6c3b11eb5dff7017b939","abstract_canon_sha256":"e9518aa1e4f107f637fdacb280a333d2d26073df0c95144688face6170891fc3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:40:47.119004Z","signature_b64":"ixYtPur2lDsds5CMYqeDDMeGp8IHRy770enzK0+8Z1asuG128nvuHw7lxerGdjW1FgtjfwEKWyYjT2qjpwT5BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b571e2d8203d34db967aa202fecb979af8e7751421a025041e889312b9765f34","last_reissued_at":"2026-07-05T09:40:47.118513Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:40:47.118513Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"physics.flu-dyn","authors_text":"Ashton Hetherington, Javier L\\'opez Leon\\'es, Soledad Le Clainche","submitted_at":"2024-11-26T13:43:50Z","abstract_excerpt":"This article introduces a novel methodology that integrates singular value decomposition (SVD) with a shallow linear neural network for forecasting high resolution fluid mechanics data. The method, termed LC-SVD-DLinear, combines a low-cost variant of singular value decomposition (LC-SVD) with the DLinear architecture, which decomposes the input features-specifically, the temporal coefficients-into trend and seasonality components, enabling a shallow neural network to capture the non-linear dynamics of the temporal data. This methodology uses under-resolved data, which can either be input dire"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.17433","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/2411.17433/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":"2411.17433","created_at":"2026-07-05T09:40:47.118573+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.17433v1","created_at":"2026-07-05T09:40:47.118573+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.17433","created_at":"2026-07-05T09:40:47.118573+00:00"},{"alias_kind":"pith_short_12","alias_value":"WVY6FWBAHU2N","created_at":"2026-07-05T09:40:47.118573+00:00"},{"alias_kind":"pith_short_16","alias_value":"WVY6FWBAHU2NXFT2","created_at":"2026-07-05T09:40:47.118573+00:00"},{"alias_kind":"pith_short_8","alias_value":"WVY6FWBA","created_at":"2026-07-05T09:40:47.118573+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/WVY6FWBAHU2NXFT2UIBP5S4XTL","json":"https://pith.science/pith/WVY6FWBAHU2NXFT2UIBP5S4XTL.json","graph_json":"https://pith.science/api/pith-number/WVY6FWBAHU2NXFT2UIBP5S4XTL/graph.json","events_json":"https://pith.science/api/pith-number/WVY6FWBAHU2NXFT2UIBP5S4XTL/events.json","paper":"https://pith.science/paper/WVY6FWBA"},"agent_actions":{"view_html":"https://pith.science/pith/WVY6FWBAHU2NXFT2UIBP5S4XTL","download_json":"https://pith.science/pith/WVY6FWBAHU2NXFT2UIBP5S4XTL.json","view_paper":"https://pith.science/paper/WVY6FWBA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.17433&json=true","fetch_graph":"https://pith.science/api/pith-number/WVY6FWBAHU2NXFT2UIBP5S4XTL/graph.json","fetch_events":"https://pith.science/api/pith-number/WVY6FWBAHU2NXFT2UIBP5S4XTL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WVY6FWBAHU2NXFT2UIBP5S4XTL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WVY6FWBAHU2NXFT2UIBP5S4XTL/action/storage_attestation","attest_author":"https://pith.science/pith/WVY6FWBAHU2NXFT2UIBP5S4XTL/action/author_attestation","sign_citation":"https://pith.science/pith/WVY6FWBAHU2NXFT2UIBP5S4XTL/action/citation_signature","submit_replication":"https://pith.science/pith/WVY6FWBAHU2NXFT2UIBP5S4XTL/action/replication_record"}},"created_at":"2026-07-05T09:40:47.118573+00:00","updated_at":"2026-07-05T09:40:47.118573+00:00"}