{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RKLU37LXGJPE6KBYMOHAVZI4FQ","short_pith_number":"pith:RKLU37LX","schema_version":"1.0","canonical_sha256":"8a974dfd77325e4f2838638e0ae51c2c1a994bb01f3fd83074d03a53d3e54c01","source":{"kind":"arxiv","id":"2405.12380","version":2},"attestation_state":"computed","paper":{"title":"Fast meta-solvers for 3D complex-shape scatterers using neural operators trained on a non-scattering problem","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"cs.LG","authors_text":"Adar Kahana, Eli Turkel, George Em Karniadakis, Jay Pathak, Rishikesh Ranade, Shanqing Liu, Youngkyu Lee, Zongren Zou","submitted_at":"2024-05-20T21:20:28Z","abstract_excerpt":"Three-dimensional target identification using scattering techniques requires high accuracy solutions and very fast computations for real-time predictions in some critical applications. We first train a deep neural operator~(DeepONet) to solve wave propagation problems described by the Helmholtz equation in a domain \\textit{without scatterers} but at different wavenumbers and with a complex absorbing boundary condition. We then design two classes of fast meta-solvers by combining DeepONet with either relaxation methods, such as Jacobi and Gauss-Seidel, or with Krylov methods, such as GMRES and "},"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":"2405.12380","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-20T21:20:28Z","cross_cats_sorted":["physics.comp-ph"],"title_canon_sha256":"dd888ce71495aed4ca6b70ca097928ce271560892feff4f26c602d37019578c6","abstract_canon_sha256":"3ee2ce81f950d2f2fcf3deb169bf9265f09612804ea38cfe3f70ed563434ed68"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:39:03.088142Z","signature_b64":"Qc/DhpZop9B3O9sI6OEmNRvVG7+krImGk0ZXB3BxWmBHuIbSOAzYm8gzGWE6SLIOrXfWh4d8tDFV+BMAh+FkCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8a974dfd77325e4f2838638e0ae51c2c1a994bb01f3fd83074d03a53d3e54c01","last_reissued_at":"2026-07-05T11:39:03.087641Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:39:03.087641Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast meta-solvers for 3D complex-shape scatterers using neural operators trained on a non-scattering problem","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"cs.LG","authors_text":"Adar Kahana, Eli Turkel, George Em Karniadakis, Jay Pathak, Rishikesh Ranade, Shanqing Liu, Youngkyu Lee, Zongren Zou","submitted_at":"2024-05-20T21:20:28Z","abstract_excerpt":"Three-dimensional target identification using scattering techniques requires high accuracy solutions and very fast computations for real-time predictions in some critical applications. We first train a deep neural operator~(DeepONet) to solve wave propagation problems described by the Helmholtz equation in a domain \\textit{without scatterers} but at different wavenumbers and with a complex absorbing boundary condition. We then design two classes of fast meta-solvers by combining DeepONet with either relaxation methods, such as Jacobi and Gauss-Seidel, or with Krylov methods, such as GMRES and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.12380","kind":"arxiv","version":2},"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/2405.12380/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":"2405.12380","created_at":"2026-07-05T11:39:03.087703+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.12380v2","created_at":"2026-07-05T11:39:03.087703+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.12380","created_at":"2026-07-05T11:39:03.087703+00:00"},{"alias_kind":"pith_short_12","alias_value":"RKLU37LXGJPE","created_at":"2026-07-05T11:39:03.087703+00:00"},{"alias_kind":"pith_short_16","alias_value":"RKLU37LXGJPE6KBY","created_at":"2026-07-05T11:39:03.087703+00:00"},{"alias_kind":"pith_short_8","alias_value":"RKLU37LX","created_at":"2026-07-05T11:39:03.087703+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2510.21804","citing_title":"XRePIT: A deep learning-computational fluid dynamics hybrid framework implemented in OpenFOAM for fast, robust, and scalable unsteady simulations","ref_index":33,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RKLU37LXGJPE6KBYMOHAVZI4FQ","json":"https://pith.science/pith/RKLU37LXGJPE6KBYMOHAVZI4FQ.json","graph_json":"https://pith.science/api/pith-number/RKLU37LXGJPE6KBYMOHAVZI4FQ/graph.json","events_json":"https://pith.science/api/pith-number/RKLU37LXGJPE6KBYMOHAVZI4FQ/events.json","paper":"https://pith.science/paper/RKLU37LX"},"agent_actions":{"view_html":"https://pith.science/pith/RKLU37LXGJPE6KBYMOHAVZI4FQ","download_json":"https://pith.science/pith/RKLU37LXGJPE6KBYMOHAVZI4FQ.json","view_paper":"https://pith.science/paper/RKLU37LX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.12380&json=true","fetch_graph":"https://pith.science/api/pith-number/RKLU37LXGJPE6KBYMOHAVZI4FQ/graph.json","fetch_events":"https://pith.science/api/pith-number/RKLU37LXGJPE6KBYMOHAVZI4FQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RKLU37LXGJPE6KBYMOHAVZI4FQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RKLU37LXGJPE6KBYMOHAVZI4FQ/action/storage_attestation","attest_author":"https://pith.science/pith/RKLU37LXGJPE6KBYMOHAVZI4FQ/action/author_attestation","sign_citation":"https://pith.science/pith/RKLU37LXGJPE6KBYMOHAVZI4FQ/action/citation_signature","submit_replication":"https://pith.science/pith/RKLU37LXGJPE6KBYMOHAVZI4FQ/action/replication_record"}},"created_at":"2026-07-05T11:39:03.087703+00:00","updated_at":"2026-07-05T11:39:03.087703+00:00"}