{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:J4SPCIPYR72S5Q46MHFV5VMWXI","short_pith_number":"pith:J4SPCIPY","schema_version":"1.0","canonical_sha256":"4f24f121f88ff52ec39e61cb5ed596ba219751e3c6b9e0bff55d160b557cf66b","source":{"kind":"arxiv","id":"2505.12557","version":1},"attestation_state":"computed","paper":{"title":"Acoustic Field Reconstruction in Tubes via Physics-Informed Neural Networks","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.SD","eess.SP","physics.app-ph"],"primary_cat":"eess.AS","authors_text":"Gary Scavone, Kazuya Yokota, Xinmeng Luan","submitted_at":"2025-05-18T22:07:44Z","abstract_excerpt":"This study investigates the application of Physics-Informed Neural Networks (PINNs) to inverse problems in acoustic tube analysis, focusing on reconstructing acoustic fields from noisy and limited observation data. Specifically, we address scenarios where the radiation model is unknown, and pressure data is only available at the tube's radiation end. A PINNs framework is proposed to reconstruct the acoustic field, along with the PINN Fine-Tuning Method (PINN-FTM) and a traditional optimization method (TOM) for predicting radiation model coefficients. The results demonstrate that PINNs can effe"},"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":"2505.12557","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.AS","submitted_at":"2025-05-18T22:07:44Z","cross_cats_sorted":["cs.SD","eess.SP","physics.app-ph"],"title_canon_sha256":"a5e9db6324316da67b9d3e72865dbc3ebdbec4f30785d1631d969ba37079d2cc","abstract_canon_sha256":"a8bafb17334f4e585c776f599719b87775b976cfdd66bc257216a0303cffb574"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:05:06.014624Z","signature_b64":"xf8FZf8hyTSP54BaB6Dh5e1XJskMs+dDSg/gmwhSSE89a2RO0jvkJ2UrUUK7ZSQdd5aWg/3nakstuDIwhqtECw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4f24f121f88ff52ec39e61cb5ed596ba219751e3c6b9e0bff55d160b557cf66b","last_reissued_at":"2026-07-05T11:05:06.014171Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:05:06.014171Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Acoustic Field Reconstruction in Tubes via Physics-Informed Neural Networks","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.SD","eess.SP","physics.app-ph"],"primary_cat":"eess.AS","authors_text":"Gary Scavone, Kazuya Yokota, Xinmeng Luan","submitted_at":"2025-05-18T22:07:44Z","abstract_excerpt":"This study investigates the application of Physics-Informed Neural Networks (PINNs) to inverse problems in acoustic tube analysis, focusing on reconstructing acoustic fields from noisy and limited observation data. Specifically, we address scenarios where the radiation model is unknown, and pressure data is only available at the tube's radiation end. A PINNs framework is proposed to reconstruct the acoustic field, along with the PINN Fine-Tuning Method (PINN-FTM) and a traditional optimization method (TOM) for predicting radiation model coefficients. The results demonstrate that PINNs can effe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.12557","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/2505.12557/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":"2505.12557","created_at":"2026-07-05T11:05:06.014234+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.12557v1","created_at":"2026-07-05T11:05:06.014234+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.12557","created_at":"2026-07-05T11:05:06.014234+00:00"},{"alias_kind":"pith_short_12","alias_value":"J4SPCIPYR72S","created_at":"2026-07-05T11:05:06.014234+00:00"},{"alias_kind":"pith_short_16","alias_value":"J4SPCIPYR72S5Q46","created_at":"2026-07-05T11:05:06.014234+00:00"},{"alias_kind":"pith_short_8","alias_value":"J4SPCIPY","created_at":"2026-07-05T11:05:06.014234+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.00400","citing_title":"Deep Learning for Personalized Binaural Audio Reproduction","ref_index":254,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/J4SPCIPYR72S5Q46MHFV5VMWXI","json":"https://pith.science/pith/J4SPCIPYR72S5Q46MHFV5VMWXI.json","graph_json":"https://pith.science/api/pith-number/J4SPCIPYR72S5Q46MHFV5VMWXI/graph.json","events_json":"https://pith.science/api/pith-number/J4SPCIPYR72S5Q46MHFV5VMWXI/events.json","paper":"https://pith.science/paper/J4SPCIPY"},"agent_actions":{"view_html":"https://pith.science/pith/J4SPCIPYR72S5Q46MHFV5VMWXI","download_json":"https://pith.science/pith/J4SPCIPYR72S5Q46MHFV5VMWXI.json","view_paper":"https://pith.science/paper/J4SPCIPY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.12557&json=true","fetch_graph":"https://pith.science/api/pith-number/J4SPCIPYR72S5Q46MHFV5VMWXI/graph.json","fetch_events":"https://pith.science/api/pith-number/J4SPCIPYR72S5Q46MHFV5VMWXI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J4SPCIPYR72S5Q46MHFV5VMWXI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J4SPCIPYR72S5Q46MHFV5VMWXI/action/storage_attestation","attest_author":"https://pith.science/pith/J4SPCIPYR72S5Q46MHFV5VMWXI/action/author_attestation","sign_citation":"https://pith.science/pith/J4SPCIPYR72S5Q46MHFV5VMWXI/action/citation_signature","submit_replication":"https://pith.science/pith/J4SPCIPYR72S5Q46MHFV5VMWXI/action/replication_record"}},"created_at":"2026-07-05T11:05:06.014234+00:00","updated_at":"2026-07-05T11:05:06.014234+00:00"}