{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:J7DLCU3QIMQ3VTTZFBQWRUUELJ","short_pith_number":"pith:J7DLCU3Q","schema_version":"1.0","canonical_sha256":"4fc6b153704321bace79286168d2845a6ab1a353c0667fa11b899510d580ef68","source":{"kind":"arxiv","id":"2412.04833","version":3},"attestation_state":"computed","paper":{"title":"Wavelet Diffusion Neural Operator","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Haodong Feng, Long Wei, Peiyan Hu, Ruiqi Feng, Rui Wang, Tailin Wu, Tao Zhang, Xiang Zheng, Yue Wang, Zhi-Ming Ma","submitted_at":"2024-12-06T07:56:25Z","abstract_excerpt":"Simulating and controlling physical systems described by partial differential equations (PDEs) are crucial tasks across science and engineering. Recently, diffusion generative models have emerged as a competitive class of methods for these tasks due to their ability to capture long-term dependencies and model high-dimensional states. However, diffusion models typically struggle with handling system states with abrupt changes and generalizing to higher resolutions. In this work, we propose Wavelet Diffusion Neural Operator (WDNO), a novel PDE simulation and control framework that enhances the h"},"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":"2412.04833","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-06T07:56:25Z","cross_cats_sorted":[],"title_canon_sha256":"d2c8e2d855f1ca679e312ac9aa2c50d12e26f3e5115f57d9937f5b2efebf5354","abstract_canon_sha256":"71a1dda6b3423215046bdebf494d8c04775c25c7c542474eaee7d396131f014f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:27:22.446533Z","signature_b64":"XQuWOjPllkgcanjWCq0TT9re+85d4VgSD0H6Ifh8SvwHmxuh01uj6yAAndukdgowCdAJP6j4fkwsmX25ipkiAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4fc6b153704321bace79286168d2845a6ab1a353c0667fa11b899510d580ef68","last_reissued_at":"2026-07-05T11:27:22.446024Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:27:22.446024Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Wavelet Diffusion Neural Operator","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Haodong Feng, Long Wei, Peiyan Hu, Ruiqi Feng, Rui Wang, Tailin Wu, Tao Zhang, Xiang Zheng, Yue Wang, Zhi-Ming Ma","submitted_at":"2024-12-06T07:56:25Z","abstract_excerpt":"Simulating and controlling physical systems described by partial differential equations (PDEs) are crucial tasks across science and engineering. Recently, diffusion generative models have emerged as a competitive class of methods for these tasks due to their ability to capture long-term dependencies and model high-dimensional states. However, diffusion models typically struggle with handling system states with abrupt changes and generalizing to higher resolutions. In this work, we propose Wavelet Diffusion Neural Operator (WDNO), a novel PDE simulation and control framework that enhances the h"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.04833","kind":"arxiv","version":3},"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/2412.04833/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":"2412.04833","created_at":"2026-07-05T11:27:22.446083+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.04833v3","created_at":"2026-07-05T11:27:22.446083+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.04833","created_at":"2026-07-05T11:27:22.446083+00:00"},{"alias_kind":"pith_short_12","alias_value":"J7DLCU3QIMQ3","created_at":"2026-07-05T11:27:22.446083+00:00"},{"alias_kind":"pith_short_16","alias_value":"J7DLCU3QIMQ3VTTZ","created_at":"2026-07-05T11:27:22.446083+00:00"},{"alias_kind":"pith_short_8","alias_value":"J7DLCU3Q","created_at":"2026-07-05T11:27:22.446083+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.25949","citing_title":"Small Models, Strong Priors: Architectural Inductive Bias for Parameter-Efficient Neural PDE Solvers","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2605.16573","citing_title":"Wavelet Flow Matching for Multi-Scale Physics Emulation","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2509.12344","citing_title":"FEDONet : Fourier-Embedded DeepONet for Spectrally Accurate Operator Learning","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2602.11229","citing_title":"Latent Generative Solvers for Generalizable Long-Term Physics Simulation","ref_index":13,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/J7DLCU3QIMQ3VTTZFBQWRUUELJ","json":"https://pith.science/pith/J7DLCU3QIMQ3VTTZFBQWRUUELJ.json","graph_json":"https://pith.science/api/pith-number/J7DLCU3QIMQ3VTTZFBQWRUUELJ/graph.json","events_json":"https://pith.science/api/pith-number/J7DLCU3QIMQ3VTTZFBQWRUUELJ/events.json","paper":"https://pith.science/paper/J7DLCU3Q"},"agent_actions":{"view_html":"https://pith.science/pith/J7DLCU3QIMQ3VTTZFBQWRUUELJ","download_json":"https://pith.science/pith/J7DLCU3QIMQ3VTTZFBQWRUUELJ.json","view_paper":"https://pith.science/paper/J7DLCU3Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.04833&json=true","fetch_graph":"https://pith.science/api/pith-number/J7DLCU3QIMQ3VTTZFBQWRUUELJ/graph.json","fetch_events":"https://pith.science/api/pith-number/J7DLCU3QIMQ3VTTZFBQWRUUELJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J7DLCU3QIMQ3VTTZFBQWRUUELJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J7DLCU3QIMQ3VTTZFBQWRUUELJ/action/storage_attestation","attest_author":"https://pith.science/pith/J7DLCU3QIMQ3VTTZFBQWRUUELJ/action/author_attestation","sign_citation":"https://pith.science/pith/J7DLCU3QIMQ3VTTZFBQWRUUELJ/action/citation_signature","submit_replication":"https://pith.science/pith/J7DLCU3QIMQ3VTTZFBQWRUUELJ/action/replication_record"}},"created_at":"2026-07-05T11:27:22.446083+00:00","updated_at":"2026-07-05T11:27:22.446083+00:00"}