{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:P54ILRUMJZH42QYZJOZGEV3HLP","short_pith_number":"pith:P54ILRUM","schema_version":"1.0","canonical_sha256":"7f7885c68c4e4fcd43194bb26257675bfd538b94c0c3fb704ac40fbf5d877fd7","source":{"kind":"arxiv","id":"2502.17134","version":2},"attestation_state":"computed","paper":{"title":"Gabor-Enhanced Physics-Informed Neural Networks for Fast Simulations of Acoustic Wavefields","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"physics.geo-ph","authors_text":"David Pardo, Mohammad Mahdi Abedi, Tariq Alkhalifah","submitted_at":"2025-02-24T13:25:40Z","abstract_excerpt":"Physics-Informed Neural Networks (PINNs) have gained increasing attention for solving partial differential equations, including the Helmholtz equation, due to their flexibility and mesh-free formulation. However, their low-frequency bias limits their accuracy and convergence speed for high-frequency wavefield simulations. To alleviate these problems, we propose a simplified PINN framework that incorporates Gabor functions, designed to capture the oscillatory and localized nature of wavefields more effectively. Unlike previous attempts that rely on auxiliary networks to learn Gabor parameters, "},"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":"2502.17134","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.geo-ph","submitted_at":"2025-02-24T13:25:40Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"db0ed486f3e13fecbae2fc72e2f9974f950e9780ac2414043199b1ad1043105d","abstract_canon_sha256":"f36fc8349a22475251547be8f0c285fe29a2fe46694e0e1e42026c3bdb33c124"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:21:30.841956Z","signature_b64":"5+V6QOGAgRZBFH8jl6M72djzwTP3eepPEzJZu95JDtxVpCgfrwqLUEM5a3FRVaW7TrAnvF5tsUN8pbeSEW6gAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f7885c68c4e4fcd43194bb26257675bfd538b94c0c3fb704ac40fbf5d877fd7","last_reissued_at":"2026-07-05T10:21:30.841439Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:21:30.841439Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Gabor-Enhanced Physics-Informed Neural Networks for Fast Simulations of Acoustic Wavefields","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"physics.geo-ph","authors_text":"David Pardo, Mohammad Mahdi Abedi, Tariq Alkhalifah","submitted_at":"2025-02-24T13:25:40Z","abstract_excerpt":"Physics-Informed Neural Networks (PINNs) have gained increasing attention for solving partial differential equations, including the Helmholtz equation, due to their flexibility and mesh-free formulation. However, their low-frequency bias limits their accuracy and convergence speed for high-frequency wavefield simulations. To alleviate these problems, we propose a simplified PINN framework that incorporates Gabor functions, designed to capture the oscillatory and localized nature of wavefields more effectively. Unlike previous attempts that rely on auxiliary networks to learn Gabor parameters, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.17134","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/2502.17134/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":"2502.17134","created_at":"2026-07-05T10:21:30.841511+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.17134v2","created_at":"2026-07-05T10:21:30.841511+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.17134","created_at":"2026-07-05T10:21:30.841511+00:00"},{"alias_kind":"pith_short_12","alias_value":"P54ILRUMJZH4","created_at":"2026-07-05T10:21:30.841511+00:00"},{"alias_kind":"pith_short_16","alias_value":"P54ILRUMJZH42QYZ","created_at":"2026-07-05T10:21:30.841511+00:00"},{"alias_kind":"pith_short_8","alias_value":"P54ILRUM","created_at":"2026-07-05T10:21:30.841511+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2504.16553","citing_title":"Least-Squares-Embedded Optimization for Accelerated Convergence of PINNs in Acoustic Wavefield Simulations","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/P54ILRUMJZH42QYZJOZGEV3HLP","json":"https://pith.science/pith/P54ILRUMJZH42QYZJOZGEV3HLP.json","graph_json":"https://pith.science/api/pith-number/P54ILRUMJZH42QYZJOZGEV3HLP/graph.json","events_json":"https://pith.science/api/pith-number/P54ILRUMJZH42QYZJOZGEV3HLP/events.json","paper":"https://pith.science/paper/P54ILRUM"},"agent_actions":{"view_html":"https://pith.science/pith/P54ILRUMJZH42QYZJOZGEV3HLP","download_json":"https://pith.science/pith/P54ILRUMJZH42QYZJOZGEV3HLP.json","view_paper":"https://pith.science/paper/P54ILRUM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.17134&json=true","fetch_graph":"https://pith.science/api/pith-number/P54ILRUMJZH42QYZJOZGEV3HLP/graph.json","fetch_events":"https://pith.science/api/pith-number/P54ILRUMJZH42QYZJOZGEV3HLP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P54ILRUMJZH42QYZJOZGEV3HLP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P54ILRUMJZH42QYZJOZGEV3HLP/action/storage_attestation","attest_author":"https://pith.science/pith/P54ILRUMJZH42QYZJOZGEV3HLP/action/author_attestation","sign_citation":"https://pith.science/pith/P54ILRUMJZH42QYZJOZGEV3HLP/action/citation_signature","submit_replication":"https://pith.science/pith/P54ILRUMJZH42QYZJOZGEV3HLP/action/replication_record"}},"created_at":"2026-07-05T10:21:30.841511+00:00","updated_at":"2026-07-05T10:21:30.841511+00:00"}