{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:2EPEG2A7CIHAFRREZMU373FGIO","short_pith_number":"pith:2EPEG2A7","schema_version":"1.0","canonical_sha256":"d11e43681f120e02c624cb29bfeca643b77f792cb2d1f89acc99f857032a0181","source":{"kind":"arxiv","id":"2305.08832","version":1},"attestation_state":"computed","paper":{"title":"Deep Learning combined with singular value decomposition to reconstruct databases in fluid dynamics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.flu-dyn","authors_text":"Adri\\'an Corrochano, Manuel L\\'opez-Mart\\'in, Paula D\\'iaz, Soledad Le Clainche","submitted_at":"2023-05-15T17:49:02Z","abstract_excerpt":"Fluid Dynamics problems are characterized by being multidimensional and nonlinear. Therefore, experiments and numerical simulations are complex and time-consuming. Motivated by this, the need arises to find new techniques to obtain data in a simpler way and in less time. In this article, we present a novel methodology based on physical principles to reconstruct three-, four- and five-dimensional databases from a strongly sparse sensors as input. The methodology consists of combining Single Value Decomposition (SVD) with neural networks. The neural network used is characterized by a simple arch"},"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":"2305.08832","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.flu-dyn","submitted_at":"2023-05-15T17:49:02Z","cross_cats_sorted":[],"title_canon_sha256":"fe41ebce8ab596880c1ff02930b39cce29795a23e16275c75466d8bde515a13d","abstract_canon_sha256":"b357e763773b71781f8180de1061afeea65a717ed24ee751c10580473119b1d0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:10:06.961370Z","signature_b64":"VoNIpfwE9aXhWQN+IY6uIz+0YdCmnIzs38kH9v6HI08Hzl3WlJo2NSN1csnfLG+BY5NGBNtRyg6GR5SD7CmJDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d11e43681f120e02c624cb29bfeca643b77f792cb2d1f89acc99f857032a0181","last_reissued_at":"2026-07-05T06:10:06.960959Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:10:06.960959Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Learning combined with singular value decomposition to reconstruct databases in fluid dynamics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.flu-dyn","authors_text":"Adri\\'an Corrochano, Manuel L\\'opez-Mart\\'in, Paula D\\'iaz, Soledad Le Clainche","submitted_at":"2023-05-15T17:49:02Z","abstract_excerpt":"Fluid Dynamics problems are characterized by being multidimensional and nonlinear. Therefore, experiments and numerical simulations are complex and time-consuming. Motivated by this, the need arises to find new techniques to obtain data in a simpler way and in less time. In this article, we present a novel methodology based on physical principles to reconstruct three-, four- and five-dimensional databases from a strongly sparse sensors as input. The methodology consists of combining Single Value Decomposition (SVD) with neural networks. The neural network used is characterized by a simple arch"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.08832","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/2305.08832/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":"2305.08832","created_at":"2026-07-05T06:10:06.961017+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.08832v1","created_at":"2026-07-05T06:10:06.961017+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.08832","created_at":"2026-07-05T06:10:06.961017+00:00"},{"alias_kind":"pith_short_12","alias_value":"2EPEG2A7CIHA","created_at":"2026-07-05T06:10:06.961017+00:00"},{"alias_kind":"pith_short_16","alias_value":"2EPEG2A7CIHAFRRE","created_at":"2026-07-05T06:10:06.961017+00:00"},{"alias_kind":"pith_short_8","alias_value":"2EPEG2A7","created_at":"2026-07-05T06:10:06.961017+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/2EPEG2A7CIHAFRREZMU373FGIO","json":"https://pith.science/pith/2EPEG2A7CIHAFRREZMU373FGIO.json","graph_json":"https://pith.science/api/pith-number/2EPEG2A7CIHAFRREZMU373FGIO/graph.json","events_json":"https://pith.science/api/pith-number/2EPEG2A7CIHAFRREZMU373FGIO/events.json","paper":"https://pith.science/paper/2EPEG2A7"},"agent_actions":{"view_html":"https://pith.science/pith/2EPEG2A7CIHAFRREZMU373FGIO","download_json":"https://pith.science/pith/2EPEG2A7CIHAFRREZMU373FGIO.json","view_paper":"https://pith.science/paper/2EPEG2A7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.08832&json=true","fetch_graph":"https://pith.science/api/pith-number/2EPEG2A7CIHAFRREZMU373FGIO/graph.json","fetch_events":"https://pith.science/api/pith-number/2EPEG2A7CIHAFRREZMU373FGIO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2EPEG2A7CIHAFRREZMU373FGIO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2EPEG2A7CIHAFRREZMU373FGIO/action/storage_attestation","attest_author":"https://pith.science/pith/2EPEG2A7CIHAFRREZMU373FGIO/action/author_attestation","sign_citation":"https://pith.science/pith/2EPEG2A7CIHAFRREZMU373FGIO/action/citation_signature","submit_replication":"https://pith.science/pith/2EPEG2A7CIHAFRREZMU373FGIO/action/replication_record"}},"created_at":"2026-07-05T06:10:06.961017+00:00","updated_at":"2026-07-05T06:10:06.961017+00:00"}