{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:NSVKUHFRCDTY3UEWW6UYG5YOBM","short_pith_number":"pith:NSVKUHFR","schema_version":"1.0","canonical_sha256":"6caaaa1cb110e78dd096b7a983770e0b210d537db356e55d608d5469cf02ce6f","source":{"kind":"arxiv","id":"2606.24696","version":1},"attestation_state":"computed","paper":{"title":"A Physics-Informed Fourier-Wavelet Transformer for Multiscale Computational Fluid Dynamics Surrogate Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"physics.flu-dyn","authors_text":"Ming Pan, Somyajit Chakraborty, Xizhong Chen","submitted_at":"2026-06-23T15:24:05Z","abstract_excerpt":"Physics-informed surrogate models can accelerate computational fluid dynamics simulations. However, many existing methods reproduce global flow patterns more reliably than localized multiscale structures. This study presents a physics-informed Fourier-wavelet transformer for next-step velocity-field reconstruction in real-world flow benchmarks. The proposed formulation combines hybrid Fourier-wavelet spectral encoding with physics-biased self-attention based on partial differential equation residual diagnostics. It also uses self-supervised pretraining through Masked Physics Prediction and Equ"},"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":"2606.24696","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.flu-dyn","submitted_at":"2026-06-23T15:24:05Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"78da573d4900dcbd5c0900acb73f779ee17633b9d6f387232384c74e3c2992b8","abstract_canon_sha256":"275184594b8438533f6c8c82917746ab9bef3ab46cc3bc9b644a5b769d55a828"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-24T01:15:39.471004Z","signature_b64":"3fY39SVUylhKYHF1qlVZcneIQrYAOOm3moJNUPdg7uOydjjVDCcDoOqggxqn8AlHG0tpmQGhyJWGxQVjUSa0DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6caaaa1cb110e78dd096b7a983770e0b210d537db356e55d608d5469cf02ce6f","last_reissued_at":"2026-06-24T01:15:39.470648Z","signature_status":"signed_v1","first_computed_at":"2026-06-24T01:15:39.470648Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Physics-Informed Fourier-Wavelet Transformer for Multiscale Computational Fluid Dynamics Surrogate Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"physics.flu-dyn","authors_text":"Ming Pan, Somyajit Chakraborty, Xizhong Chen","submitted_at":"2026-06-23T15:24:05Z","abstract_excerpt":"Physics-informed surrogate models can accelerate computational fluid dynamics simulations. However, many existing methods reproduce global flow patterns more reliably than localized multiscale structures. This study presents a physics-informed Fourier-wavelet transformer for next-step velocity-field reconstruction in real-world flow benchmarks. The proposed formulation combines hybrid Fourier-wavelet spectral encoding with physics-biased self-attention based on partial differential equation residual diagnostics. It also uses self-supervised pretraining through Masked Physics Prediction and Equ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.24696","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/2606.24696/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":"2606.24696","created_at":"2026-06-24T01:15:39.470703+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.24696v1","created_at":"2026-06-24T01:15:39.470703+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.24696","created_at":"2026-06-24T01:15:39.470703+00:00"},{"alias_kind":"pith_short_12","alias_value":"NSVKUHFRCDTY","created_at":"2026-06-24T01:15:39.470703+00:00"},{"alias_kind":"pith_short_16","alias_value":"NSVKUHFRCDTY3UEW","created_at":"2026-06-24T01:15:39.470703+00:00"},{"alias_kind":"pith_short_8","alias_value":"NSVKUHFR","created_at":"2026-06-24T01:15:39.470703+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/NSVKUHFRCDTY3UEWW6UYG5YOBM","json":"https://pith.science/pith/NSVKUHFRCDTY3UEWW6UYG5YOBM.json","graph_json":"https://pith.science/api/pith-number/NSVKUHFRCDTY3UEWW6UYG5YOBM/graph.json","events_json":"https://pith.science/api/pith-number/NSVKUHFRCDTY3UEWW6UYG5YOBM/events.json","paper":"https://pith.science/paper/NSVKUHFR"},"agent_actions":{"view_html":"https://pith.science/pith/NSVKUHFRCDTY3UEWW6UYG5YOBM","download_json":"https://pith.science/pith/NSVKUHFRCDTY3UEWW6UYG5YOBM.json","view_paper":"https://pith.science/paper/NSVKUHFR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.24696&json=true","fetch_graph":"https://pith.science/api/pith-number/NSVKUHFRCDTY3UEWW6UYG5YOBM/graph.json","fetch_events":"https://pith.science/api/pith-number/NSVKUHFRCDTY3UEWW6UYG5YOBM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NSVKUHFRCDTY3UEWW6UYG5YOBM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NSVKUHFRCDTY3UEWW6UYG5YOBM/action/storage_attestation","attest_author":"https://pith.science/pith/NSVKUHFRCDTY3UEWW6UYG5YOBM/action/author_attestation","sign_citation":"https://pith.science/pith/NSVKUHFRCDTY3UEWW6UYG5YOBM/action/citation_signature","submit_replication":"https://pith.science/pith/NSVKUHFRCDTY3UEWW6UYG5YOBM/action/replication_record"}},"created_at":"2026-06-24T01:15:39.470703+00:00","updated_at":"2026-06-24T01:15:39.470703+00:00"}