{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:6ZTJCHBRDA34UWOXJ7DCGLC6QK","short_pith_number":"pith:6ZTJCHBR","schema_version":"1.0","canonical_sha256":"f666911c311837ca59d74fc6232c5e82bb7386b76fab9af11721a1dbcb787afa","source":{"kind":"arxiv","id":"2502.02593","version":1},"attestation_state":"computed","paper":{"title":"Reconstructing 3D Flow from 2D Data with Diffusion Transformer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","physics.flu-dyn"],"primary_cat":"cs.CE","authors_text":"Fan Lei","submitted_at":"2024-12-20T13:19:48Z","abstract_excerpt":"Fluid flow is a widely applied physical problem, crucial in various fields. Due to the highly nonlinear and chaotic nature of fluids, analyzing fluid-related problems is exceptionally challenging. Computational fluid dynamics (CFD) is the best tool for this analysis but involves significant computational resources, especially for 3D simulations, which are slow and resource-intensive. In experimental fluid dynamics, PIV cost increases with dimensionality. Reconstructing 3D flow fields from 2D PIV data could reduce costs and expand application scenarios. Here, We propose a Diffusion Transformer-"},"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":"2502.02593","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CE","submitted_at":"2024-12-20T13:19:48Z","cross_cats_sorted":["cs.AI","physics.flu-dyn"],"title_canon_sha256":"797b1629d136b58fca9ffe9220f5195d0568c1712e00db3b960b363c5f19c59c","abstract_canon_sha256":"2c3be2ea4e50d6f1fd2bffb506a5c1d0220999efc8084fdc41d5490de8775858"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:09:31.335326Z","signature_b64":"wqSqO5QhfKVXW2GVfwJBoSI9+PR7VAef3OLAI6/GB2CdO84CbgQQxctpNKywMZ9KKtcoX0eAOvb3Msp0l0eDAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f666911c311837ca59d74fc6232c5e82bb7386b76fab9af11721a1dbcb787afa","last_reissued_at":"2026-07-05T10:09:31.334858Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:09:31.334858Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Reconstructing 3D Flow from 2D Data with Diffusion Transformer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","physics.flu-dyn"],"primary_cat":"cs.CE","authors_text":"Fan Lei","submitted_at":"2024-12-20T13:19:48Z","abstract_excerpt":"Fluid flow is a widely applied physical problem, crucial in various fields. Due to the highly nonlinear and chaotic nature of fluids, analyzing fluid-related problems is exceptionally challenging. Computational fluid dynamics (CFD) is the best tool for this analysis but involves significant computational resources, especially for 3D simulations, which are slow and resource-intensive. In experimental fluid dynamics, PIV cost increases with dimensionality. Reconstructing 3D flow fields from 2D PIV data could reduce costs and expand application scenarios. Here, We propose a Diffusion Transformer-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.02593","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/2502.02593/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":"2502.02593","created_at":"2026-07-05T10:09:31.334917+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.02593v1","created_at":"2026-07-05T10:09:31.334917+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.02593","created_at":"2026-07-05T10:09:31.334917+00:00"},{"alias_kind":"pith_short_12","alias_value":"6ZTJCHBRDA34","created_at":"2026-07-05T10:09:31.334917+00:00"},{"alias_kind":"pith_short_16","alias_value":"6ZTJCHBRDA34UWOX","created_at":"2026-07-05T10:09:31.334917+00:00"},{"alias_kind":"pith_short_8","alias_value":"6ZTJCHBR","created_at":"2026-07-05T10:09:31.334917+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/6ZTJCHBRDA34UWOXJ7DCGLC6QK","json":"https://pith.science/pith/6ZTJCHBRDA34UWOXJ7DCGLC6QK.json","graph_json":"https://pith.science/api/pith-number/6ZTJCHBRDA34UWOXJ7DCGLC6QK/graph.json","events_json":"https://pith.science/api/pith-number/6ZTJCHBRDA34UWOXJ7DCGLC6QK/events.json","paper":"https://pith.science/paper/6ZTJCHBR"},"agent_actions":{"view_html":"https://pith.science/pith/6ZTJCHBRDA34UWOXJ7DCGLC6QK","download_json":"https://pith.science/pith/6ZTJCHBRDA34UWOXJ7DCGLC6QK.json","view_paper":"https://pith.science/paper/6ZTJCHBR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.02593&json=true","fetch_graph":"https://pith.science/api/pith-number/6ZTJCHBRDA34UWOXJ7DCGLC6QK/graph.json","fetch_events":"https://pith.science/api/pith-number/6ZTJCHBRDA34UWOXJ7DCGLC6QK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6ZTJCHBRDA34UWOXJ7DCGLC6QK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6ZTJCHBRDA34UWOXJ7DCGLC6QK/action/storage_attestation","attest_author":"https://pith.science/pith/6ZTJCHBRDA34UWOXJ7DCGLC6QK/action/author_attestation","sign_citation":"https://pith.science/pith/6ZTJCHBRDA34UWOXJ7DCGLC6QK/action/citation_signature","submit_replication":"https://pith.science/pith/6ZTJCHBRDA34UWOXJ7DCGLC6QK/action/replication_record"}},"created_at":"2026-07-05T10:09:31.334917+00:00","updated_at":"2026-07-05T10:09:31.334917+00:00"}