{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:6QQU5VJIB2RIBJDX5R3ZM7ZAZM","short_pith_number":"pith:6QQU5VJI","schema_version":"1.0","canonical_sha256":"f4214ed5280ea280a477ec77967f20cb0f19d5a94bd4fb348f9be76016be4115","source":{"kind":"arxiv","id":"2404.08091","version":1},"attestation_state":"computed","paper":{"title":"Continual Learning of Range-Dependent Transmission Loss for Underwater Acoustic using Conditional Convolutional Neural Net","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.SP","physics.flu-dyn"],"primary_cat":"cs.LG","authors_text":"Akash Venkateshwaran, Indu Kant Deo, Rajeev K. Jaiman","submitted_at":"2024-04-11T19:13:38Z","abstract_excerpt":"There is a significant need for precise and reliable forecasting of the far-field noise emanating from shipping vessels. Conventional full-order models based on the Navier-Stokes equations are unsuitable, and sophisticated model reduction methods may be ineffective for accurately predicting far-field noise in environments with seamounts and significant variations in bathymetry. Recent advances in reduced-order models, particularly those based on convolutional and recurrent neural networks, offer a faster and more accurate alternative. These models use convolutional neural networks to reduce da"},"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":"2404.08091","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-04-11T19:13:38Z","cross_cats_sorted":["eess.SP","physics.flu-dyn"],"title_canon_sha256":"263dd276af21be10081a06d64ef80a93f33847e7c7ddc9dd96dea1e501dc1032","abstract_canon_sha256":"51b629a8bcbd1e407c558d6ec272aa8699c699ab2223fb5cedb569a35194b6db"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:07:17.513599Z","signature_b64":"2Wp82c2uyRGa+3q5bU/dLVydivagdlu8XlIMClI7QmIc5WsxAUcLYXkoHASQfI2vj2KrKOtGHJ6kA1ijzHVFBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f4214ed5280ea280a477ec77967f20cb0f19d5a94bd4fb348f9be76016be4115","last_reissued_at":"2026-07-05T08:07:17.513108Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:07:17.513108Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Continual Learning of Range-Dependent Transmission Loss for Underwater Acoustic using Conditional Convolutional Neural Net","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.SP","physics.flu-dyn"],"primary_cat":"cs.LG","authors_text":"Akash Venkateshwaran, Indu Kant Deo, Rajeev K. Jaiman","submitted_at":"2024-04-11T19:13:38Z","abstract_excerpt":"There is a significant need for precise and reliable forecasting of the far-field noise emanating from shipping vessels. Conventional full-order models based on the Navier-Stokes equations are unsuitable, and sophisticated model reduction methods may be ineffective for accurately predicting far-field noise in environments with seamounts and significant variations in bathymetry. Recent advances in reduced-order models, particularly those based on convolutional and recurrent neural networks, offer a faster and more accurate alternative. These models use convolutional neural networks to reduce da"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.08091","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/2404.08091/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":"2404.08091","created_at":"2026-07-05T08:07:17.513166+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.08091v1","created_at":"2026-07-05T08:07:17.513166+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.08091","created_at":"2026-07-05T08:07:17.513166+00:00"},{"alias_kind":"pith_short_12","alias_value":"6QQU5VJIB2RI","created_at":"2026-07-05T08:07:17.513166+00:00"},{"alias_kind":"pith_short_16","alias_value":"6QQU5VJIB2RIBJDX","created_at":"2026-07-05T08:07:17.513166+00:00"},{"alias_kind":"pith_short_8","alias_value":"6QQU5VJI","created_at":"2026-07-05T08:07:17.513166+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.02924","citing_title":"Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks","ref_index":8,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6QQU5VJIB2RIBJDX5R3ZM7ZAZM","json":"https://pith.science/pith/6QQU5VJIB2RIBJDX5R3ZM7ZAZM.json","graph_json":"https://pith.science/api/pith-number/6QQU5VJIB2RIBJDX5R3ZM7ZAZM/graph.json","events_json":"https://pith.science/api/pith-number/6QQU5VJIB2RIBJDX5R3ZM7ZAZM/events.json","paper":"https://pith.science/paper/6QQU5VJI"},"agent_actions":{"view_html":"https://pith.science/pith/6QQU5VJIB2RIBJDX5R3ZM7ZAZM","download_json":"https://pith.science/pith/6QQU5VJIB2RIBJDX5R3ZM7ZAZM.json","view_paper":"https://pith.science/paper/6QQU5VJI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.08091&json=true","fetch_graph":"https://pith.science/api/pith-number/6QQU5VJIB2RIBJDX5R3ZM7ZAZM/graph.json","fetch_events":"https://pith.science/api/pith-number/6QQU5VJIB2RIBJDX5R3ZM7ZAZM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6QQU5VJIB2RIBJDX5R3ZM7ZAZM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6QQU5VJIB2RIBJDX5R3ZM7ZAZM/action/storage_attestation","attest_author":"https://pith.science/pith/6QQU5VJIB2RIBJDX5R3ZM7ZAZM/action/author_attestation","sign_citation":"https://pith.science/pith/6QQU5VJIB2RIBJDX5R3ZM7ZAZM/action/citation_signature","submit_replication":"https://pith.science/pith/6QQU5VJIB2RIBJDX5R3ZM7ZAZM/action/replication_record"}},"created_at":"2026-07-05T08:07:17.513166+00:00","updated_at":"2026-07-05T08:07:17.513166+00:00"}