{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:3BGOA3ZK3NMVLNVADL7P3WRXRV","short_pith_number":"pith:3BGOA3ZK","schema_version":"1.0","canonical_sha256":"d84ce06f2adb5955b6a01afefdda378d64cf67239746760b2b450bd0c502219f","source":{"kind":"arxiv","id":"2605.27756","version":1},"attestation_state":"computed","paper":{"title":"Sparse POD Mode Selection and Manifold Dimensionality Reduction with Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.NA","math.DS","math.NA"],"primary_cat":"physics.flu-dyn","authors_text":"Elizabeth Qian, Julie Bessac, Marc T. Henry de Frahan, Prakash Mohan, Tomoki Koike","submitted_at":"2026-05-26T23:10:05Z","abstract_excerpt":"High-performance computing enables simulation of high-dimensional physical systems, but downstream analyses such as inverse problems and control remain computationally expensive, motivating model order reduction (MOR) to construct efficient low-dimensional surrogates. Proper Orthogonal Decomposition (POD), a widely adopted data-driven MOR method, projects dynamics onto linear subspaces spanned by the most energetic modes. However, POD struggles for problems with slowly decaying Kolmogorov \\(n\\)-widths, such as advection-dominated and turbulent flows, requiring many modes for accurate reconstru"},"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":"2605.27756","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.flu-dyn","submitted_at":"2026-05-26T23:10:05Z","cross_cats_sorted":["cs.LG","cs.NA","math.DS","math.NA"],"title_canon_sha256":"29c2c905444417c332d17e7434e863cc7450febef88ff13dfd7571d007704e4f","abstract_canon_sha256":"7cfaa6e18dc51c56af03a6b0ad2196c34b80961af99ec5d727d5b10acd4e0e6f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T01:04:48.041649Z","signature_b64":"75HEo/yE8DxKRt1i41vIiHTpiSTorfnQFyKA5HEU7W/4WRDF9rhiR6jQFuFLLcsyysIhRfBfrfmF+UECCTWyAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d84ce06f2adb5955b6a01afefdda378d64cf67239746760b2b450bd0c502219f","last_reissued_at":"2026-05-28T01:04:48.041229Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T01:04:48.041229Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sparse POD Mode Selection and Manifold Dimensionality Reduction with Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.NA","math.DS","math.NA"],"primary_cat":"physics.flu-dyn","authors_text":"Elizabeth Qian, Julie Bessac, Marc T. Henry de Frahan, Prakash Mohan, Tomoki Koike","submitted_at":"2026-05-26T23:10:05Z","abstract_excerpt":"High-performance computing enables simulation of high-dimensional physical systems, but downstream analyses such as inverse problems and control remain computationally expensive, motivating model order reduction (MOR) to construct efficient low-dimensional surrogates. Proper Orthogonal Decomposition (POD), a widely adopted data-driven MOR method, projects dynamics onto linear subspaces spanned by the most energetic modes. However, POD struggles for problems with slowly decaying Kolmogorov \\(n\\)-widths, such as advection-dominated and turbulent flows, requiring many modes for accurate reconstru"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.27756","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/2605.27756/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":"2605.27756","created_at":"2026-05-28T01:04:48.041282+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.27756v1","created_at":"2026-05-28T01:04:48.041282+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.27756","created_at":"2026-05-28T01:04:48.041282+00:00"},{"alias_kind":"pith_short_12","alias_value":"3BGOA3ZK3NMV","created_at":"2026-05-28T01:04:48.041282+00:00"},{"alias_kind":"pith_short_16","alias_value":"3BGOA3ZK3NMVLNVA","created_at":"2026-05-28T01:04:48.041282+00:00"},{"alias_kind":"pith_short_8","alias_value":"3BGOA3ZK","created_at":"2026-05-28T01:04:48.041282+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/3BGOA3ZK3NMVLNVADL7P3WRXRV","json":"https://pith.science/pith/3BGOA3ZK3NMVLNVADL7P3WRXRV.json","graph_json":"https://pith.science/api/pith-number/3BGOA3ZK3NMVLNVADL7P3WRXRV/graph.json","events_json":"https://pith.science/api/pith-number/3BGOA3ZK3NMVLNVADL7P3WRXRV/events.json","paper":"https://pith.science/paper/3BGOA3ZK"},"agent_actions":{"view_html":"https://pith.science/pith/3BGOA3ZK3NMVLNVADL7P3WRXRV","download_json":"https://pith.science/pith/3BGOA3ZK3NMVLNVADL7P3WRXRV.json","view_paper":"https://pith.science/paper/3BGOA3ZK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.27756&json=true","fetch_graph":"https://pith.science/api/pith-number/3BGOA3ZK3NMVLNVADL7P3WRXRV/graph.json","fetch_events":"https://pith.science/api/pith-number/3BGOA3ZK3NMVLNVADL7P3WRXRV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3BGOA3ZK3NMVLNVADL7P3WRXRV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3BGOA3ZK3NMVLNVADL7P3WRXRV/action/storage_attestation","attest_author":"https://pith.science/pith/3BGOA3ZK3NMVLNVADL7P3WRXRV/action/author_attestation","sign_citation":"https://pith.science/pith/3BGOA3ZK3NMVLNVADL7P3WRXRV/action/citation_signature","submit_replication":"https://pith.science/pith/3BGOA3ZK3NMVLNVADL7P3WRXRV/action/replication_record"}},"created_at":"2026-05-28T01:04:48.041282+00:00","updated_at":"2026-05-28T01:04:48.041282+00:00"}