{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:BVYJJX5HVLTXXVCZDI7AGEZE5F","short_pith_number":"pith:BVYJJX5H","schema_version":"1.0","canonical_sha256":"0d7094dfa7aae77bd4591a3e031324e9468b68ae70601b1e7390b4ba9dfc433c","source":{"kind":"arxiv","id":"2503.22600","version":1},"attestation_state":"computed","paper":{"title":"Generative Latent Neural PDE Solver using Flow Matching","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Amir Barati Farimani, Anthony Zhou, Zijie Li","submitted_at":"2025-03-28T16:44:28Z","abstract_excerpt":"Autoregressive next-step prediction models have become the de-facto standard for building data-driven neural solvers to forecast time-dependent partial differential equations (PDEs). Denoise training that is closely related to diffusion probabilistic model has been shown to enhance the temporal stability of neural solvers, while its stochastic inference mechanism enables ensemble predictions and uncertainty quantification. In principle, such training involves sampling a series of discretized diffusion timesteps during both training and inference, inevitably increasing computational overhead. I"},"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":"2503.22600","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-28T16:44:28Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9ffeaf51d5e507f33ef8ac2ff45fbd7453957810c80d16d99436cddb6ab0933b","abstract_canon_sha256":"dbbe32a8b5be7b40b08d9966ced54e8371e396d90949242c1bff1fa221c1bf33"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:41:07.447573Z","signature_b64":"xBOex4j+Fy0X7sAAhamZqPOrVEnJvFaflhMcOLuZKehFiLlwRVMsL9dogkF5X6uzug6p4uVE5ZmhYbxrJGEbDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0d7094dfa7aae77bd4591a3e031324e9468b68ae70601b1e7390b4ba9dfc433c","last_reissued_at":"2026-07-05T10:41:07.447005Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:41:07.447005Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generative Latent Neural PDE Solver using Flow Matching","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Amir Barati Farimani, Anthony Zhou, Zijie Li","submitted_at":"2025-03-28T16:44:28Z","abstract_excerpt":"Autoregressive next-step prediction models have become the de-facto standard for building data-driven neural solvers to forecast time-dependent partial differential equations (PDEs). Denoise training that is closely related to diffusion probabilistic model has been shown to enhance the temporal stability of neural solvers, while its stochastic inference mechanism enables ensemble predictions and uncertainty quantification. In principle, such training involves sampling a series of discretized diffusion timesteps during both training and inference, inevitably increasing computational overhead. I"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.22600","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/2503.22600/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":"2503.22600","created_at":"2026-07-05T10:41:07.447090+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.22600v1","created_at":"2026-07-05T10:41:07.447090+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.22600","created_at":"2026-07-05T10:41:07.447090+00:00"},{"alias_kind":"pith_short_12","alias_value":"BVYJJX5HVLTX","created_at":"2026-07-05T10:41:07.447090+00:00"},{"alias_kind":"pith_short_16","alias_value":"BVYJJX5HVLTXXVCZ","created_at":"2026-07-05T10:41:07.447090+00:00"},{"alias_kind":"pith_short_8","alias_value":"BVYJJX5H","created_at":"2026-07-05T10:41:07.447090+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2509.24517","citing_title":"Physics Priors Offer Useful Accuracy-Carbon Trade-Offs in Spatio-Temporal Forecasting","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2605.16573","citing_title":"Wavelet Flow Matching for Multi-Scale Physics Emulation","ref_index":27,"is_internal_anchor":false},{"citing_arxiv_id":"2605.03548","citing_title":"PerFlow: Physics-Embedded Rectified Flow for Efficient Reconstruction and Uncertainty Quantification of Spatiotemporal Dynamics","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2509.18611","citing_title":"Flow marching for a generative PDE foundation model","ref_index":36,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13790","citing_title":"Di-BiLPS: Denoising induced Bidirectional Latent-PDE-Solver under Sparse Observations","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07366","citing_title":"Flow Learners for PDEs: Toward a Physics-to-Physics Paradigm for Scientific Computing","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08454","citing_title":"Recovering Physical Dynamics from Discrete Observations via Intrinsic Differential Consistency","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2605.03548","citing_title":"PerFlow: Physics-Embedded Rectified Flow for Efficient Reconstruction and Uncertainty Quantification of Spatiotemporal Dynamics","ref_index":13,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BVYJJX5HVLTXXVCZDI7AGEZE5F","json":"https://pith.science/pith/BVYJJX5HVLTXXVCZDI7AGEZE5F.json","graph_json":"https://pith.science/api/pith-number/BVYJJX5HVLTXXVCZDI7AGEZE5F/graph.json","events_json":"https://pith.science/api/pith-number/BVYJJX5HVLTXXVCZDI7AGEZE5F/events.json","paper":"https://pith.science/paper/BVYJJX5H"},"agent_actions":{"view_html":"https://pith.science/pith/BVYJJX5HVLTXXVCZDI7AGEZE5F","download_json":"https://pith.science/pith/BVYJJX5HVLTXXVCZDI7AGEZE5F.json","view_paper":"https://pith.science/paper/BVYJJX5H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.22600&json=true","fetch_graph":"https://pith.science/api/pith-number/BVYJJX5HVLTXXVCZDI7AGEZE5F/graph.json","fetch_events":"https://pith.science/api/pith-number/BVYJJX5HVLTXXVCZDI7AGEZE5F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BVYJJX5HVLTXXVCZDI7AGEZE5F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BVYJJX5HVLTXXVCZDI7AGEZE5F/action/storage_attestation","attest_author":"https://pith.science/pith/BVYJJX5HVLTXXVCZDI7AGEZE5F/action/author_attestation","sign_citation":"https://pith.science/pith/BVYJJX5HVLTXXVCZDI7AGEZE5F/action/citation_signature","submit_replication":"https://pith.science/pith/BVYJJX5HVLTXXVCZDI7AGEZE5F/action/replication_record"}},"created_at":"2026-07-05T10:41:07.447090+00:00","updated_at":"2026-07-05T10:41:07.447090+00:00"}