{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:XHLJU7BEBWE6MTMP2RGJGWRJDE","short_pith_number":"pith:XHLJU7BE","schema_version":"1.0","canonical_sha256":"b9d69a7c240d89e64d8fd44c935a2919022ba6cbd5957e03b0b16117f5b096f0","source":{"kind":"arxiv","id":"2309.10117","version":1},"attestation_state":"computed","paper":{"title":"Deep smoothness WENO scheme for two-dimensional hyperbolic conservation laws: A deep learning approach for learning smoothness indicators","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.NA"],"primary_cat":"math.NA","authors_text":"Ameya D. Jagtap, Matthias Ehrhardt, Tatiana Kossaczk\\'a","submitted_at":"2023-09-18T19:42:35Z","abstract_excerpt":"In this paper, we introduce an improved version of the fifth-order weighted essentially non-oscillatory (WENO) shock-capturing scheme by incorporating deep learning techniques. The established WENO algorithm is improved by training a compact neural network to adjust the smoothness indicators within the WENO scheme. This modification enhances the accuracy of the numerical results, particularly near abrupt shocks. Unlike previous deep learning-based methods, no additional post-processing steps are necessary for maintaining consistency. We demonstrate the superiority of our new approach using sev"},"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":"2309.10117","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.NA","submitted_at":"2023-09-18T19:42:35Z","cross_cats_sorted":["cs.LG","cs.NA"],"title_canon_sha256":"2cd5f1b17f0cc766e93f2520b7a16c3d2fecb4e056fe96554b512a8ae21a8c66","abstract_canon_sha256":"4500df63a6af55ba2371585dc7807eae479d7d17d8ca493992fd73c731d9d4f9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:51:51.148222Z","signature_b64":"S1pE4u7ndNo1rTzHK2GJ9VgkWX9lrSGy13bd6ZKugEi+jzzYIVaA2s1ZrBygVUZMlYrmNQ8fAIFxmH1PuEZjCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b9d69a7c240d89e64d8fd44c935a2919022ba6cbd5957e03b0b16117f5b096f0","last_reissued_at":"2026-07-05T06:51:51.147808Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:51:51.147808Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep smoothness WENO scheme for two-dimensional hyperbolic conservation laws: A deep learning approach for learning smoothness indicators","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.NA"],"primary_cat":"math.NA","authors_text":"Ameya D. Jagtap, Matthias Ehrhardt, Tatiana Kossaczk\\'a","submitted_at":"2023-09-18T19:42:35Z","abstract_excerpt":"In this paper, we introduce an improved version of the fifth-order weighted essentially non-oscillatory (WENO) shock-capturing scheme by incorporating deep learning techniques. The established WENO algorithm is improved by training a compact neural network to adjust the smoothness indicators within the WENO scheme. This modification enhances the accuracy of the numerical results, particularly near abrupt shocks. Unlike previous deep learning-based methods, no additional post-processing steps are necessary for maintaining consistency. We demonstrate the superiority of our new approach using sev"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.10117","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/2309.10117/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":"2309.10117","created_at":"2026-07-05T06:51:51.147865+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.10117v1","created_at":"2026-07-05T06:51:51.147865+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.10117","created_at":"2026-07-05T06:51:51.147865+00:00"},{"alias_kind":"pith_short_12","alias_value":"XHLJU7BEBWE6","created_at":"2026-07-05T06:51:51.147865+00:00"},{"alias_kind":"pith_short_16","alias_value":"XHLJU7BEBWE6MTMP","created_at":"2026-07-05T06:51:51.147865+00:00"},{"alias_kind":"pith_short_8","alias_value":"XHLJU7BE","created_at":"2026-07-05T06:51:51.147865+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XHLJU7BEBWE6MTMP2RGJGWRJDE","json":"https://pith.science/pith/XHLJU7BEBWE6MTMP2RGJGWRJDE.json","graph_json":"https://pith.science/api/pith-number/XHLJU7BEBWE6MTMP2RGJGWRJDE/graph.json","events_json":"https://pith.science/api/pith-number/XHLJU7BEBWE6MTMP2RGJGWRJDE/events.json","paper":"https://pith.science/paper/XHLJU7BE"},"agent_actions":{"view_html":"https://pith.science/pith/XHLJU7BEBWE6MTMP2RGJGWRJDE","download_json":"https://pith.science/pith/XHLJU7BEBWE6MTMP2RGJGWRJDE.json","view_paper":"https://pith.science/paper/XHLJU7BE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.10117&json=true","fetch_graph":"https://pith.science/api/pith-number/XHLJU7BEBWE6MTMP2RGJGWRJDE/graph.json","fetch_events":"https://pith.science/api/pith-number/XHLJU7BEBWE6MTMP2RGJGWRJDE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XHLJU7BEBWE6MTMP2RGJGWRJDE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XHLJU7BEBWE6MTMP2RGJGWRJDE/action/storage_attestation","attest_author":"https://pith.science/pith/XHLJU7BEBWE6MTMP2RGJGWRJDE/action/author_attestation","sign_citation":"https://pith.science/pith/XHLJU7BEBWE6MTMP2RGJGWRJDE/action/citation_signature","submit_replication":"https://pith.science/pith/XHLJU7BEBWE6MTMP2RGJGWRJDE/action/replication_record"}},"created_at":"2026-07-05T06:51:51.147865+00:00","updated_at":"2026-07-05T06:51:51.147865+00:00"}