{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:F7Q3MAIW6EVAEFFCAJHOO5N6RT","short_pith_number":"pith:F7Q3MAIW","schema_version":"1.0","canonical_sha256":"2fe1b60116f12a0214a2024ee775be8cd7167d38a450c4a37da28e7422ed15e6","source":{"kind":"arxiv","id":"2509.08191","version":1},"attestation_state":"computed","paper":{"title":"Rollout-LaSDI: Enhancing the long-term accuracy of Latent Space Dynamics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Robert Stephany, Youngsoo Choi","submitted_at":"2025-09-09T23:46:25Z","abstract_excerpt":"Solving complex partial differential equations is vital in the physical sciences, but often requires computationally expensive numerical methods. Reduced-order models (ROMs) address this by exploiting dimensionality reduction to create fast approximations. While modern ROMs can solve parameterized families of PDEs, their predictive power degrades over long time horizons. We address this by (1) introducing a flexible, high-order, yet inexpensive finite-difference scheme and (2) proposing a Rollout loss that trains ROMs to make accurate predictions over arbitrary time horizons. We demonstrate ou"},"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":"2509.08191","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-09T23:46:25Z","cross_cats_sorted":[],"title_canon_sha256":"2bd0198eee25369cdc9c1449a5e8dbfd11e6ffa01fa3226be783a66c68255d3f","abstract_canon_sha256":"19f5a9ad9e3e02bed0f606e2b137d862383e385c8a696f27ee1eef34432dfc1e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:08:24.689943Z","signature_b64":"vFln/jxrXBy6jP8pfU02d5zpbrx5YB+YK63lXH3CjZ7WyTfWAZ8LivaBY+fSiRe9ioE226k+hisWPksfK3jGAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2fe1b60116f12a0214a2024ee775be8cd7167d38a450c4a37da28e7422ed15e6","last_reissued_at":"2026-07-05T12:08:24.689384Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:08:24.689384Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rollout-LaSDI: Enhancing the long-term accuracy of Latent Space Dynamics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Robert Stephany, Youngsoo Choi","submitted_at":"2025-09-09T23:46:25Z","abstract_excerpt":"Solving complex partial differential equations is vital in the physical sciences, but often requires computationally expensive numerical methods. Reduced-order models (ROMs) address this by exploiting dimensionality reduction to create fast approximations. While modern ROMs can solve parameterized families of PDEs, their predictive power degrades over long time horizons. We address this by (1) introducing a flexible, high-order, yet inexpensive finite-difference scheme and (2) proposing a Rollout loss that trains ROMs to make accurate predictions over arbitrary time horizons. We demonstrate ou"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.08191","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/2509.08191/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":"2509.08191","created_at":"2026-07-05T12:08:24.689446+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.08191v1","created_at":"2026-07-05T12:08:24.689446+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.08191","created_at":"2026-07-05T12:08:24.689446+00:00"},{"alias_kind":"pith_short_12","alias_value":"F7Q3MAIW6EVA","created_at":"2026-07-05T12:08:24.689446+00:00"},{"alias_kind":"pith_short_16","alias_value":"F7Q3MAIW6EVAEFFC","created_at":"2026-07-05T12:08:24.689446+00:00"},{"alias_kind":"pith_short_8","alias_value":"F7Q3MAIW","created_at":"2026-07-05T12:08:24.689446+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2512.15997","citing_title":"Higher-Order LaSDI: Reduced Order Modeling with Multiple Time Derivatives","ref_index":45,"is_internal_anchor":false},{"citing_arxiv_id":"2605.20639","citing_title":"Time-Dependent PDE-Constrained Optimization via Weak-Form Latent Dynamics","ref_index":25,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/F7Q3MAIW6EVAEFFCAJHOO5N6RT","json":"https://pith.science/pith/F7Q3MAIW6EVAEFFCAJHOO5N6RT.json","graph_json":"https://pith.science/api/pith-number/F7Q3MAIW6EVAEFFCAJHOO5N6RT/graph.json","events_json":"https://pith.science/api/pith-number/F7Q3MAIW6EVAEFFCAJHOO5N6RT/events.json","paper":"https://pith.science/paper/F7Q3MAIW"},"agent_actions":{"view_html":"https://pith.science/pith/F7Q3MAIW6EVAEFFCAJHOO5N6RT","download_json":"https://pith.science/pith/F7Q3MAIW6EVAEFFCAJHOO5N6RT.json","view_paper":"https://pith.science/paper/F7Q3MAIW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.08191&json=true","fetch_graph":"https://pith.science/api/pith-number/F7Q3MAIW6EVAEFFCAJHOO5N6RT/graph.json","fetch_events":"https://pith.science/api/pith-number/F7Q3MAIW6EVAEFFCAJHOO5N6RT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/F7Q3MAIW6EVAEFFCAJHOO5N6RT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/F7Q3MAIW6EVAEFFCAJHOO5N6RT/action/storage_attestation","attest_author":"https://pith.science/pith/F7Q3MAIW6EVAEFFCAJHOO5N6RT/action/author_attestation","sign_citation":"https://pith.science/pith/F7Q3MAIW6EVAEFFCAJHOO5N6RT/action/citation_signature","submit_replication":"https://pith.science/pith/F7Q3MAIW6EVAEFFCAJHOO5N6RT/action/replication_record"}},"created_at":"2026-07-05T12:08:24.689446+00:00","updated_at":"2026-07-05T12:08:24.689446+00:00"}