{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PJQKB5BJ4GKS6EBJRVPQDXCU2L","short_pith_number":"pith:PJQKB5BJ","schema_version":"1.0","canonical_sha256":"7a60a0f429e1952f10298d5f01dc54d2fcce7aaf5f936ea7cb22f52d9657e511","source":{"kind":"arxiv","id":"2403.11746","version":1},"attestation_state":"computed","paper":{"title":"Revisiting Tensor Basis Neural Networks for Reynolds stress modeling: application to plane channel and square duct flows","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["physics.comp-ph","physics.data-an"],"primary_cat":"physics.flu-dyn","authors_text":"Didier Lucor, Guillaume Damblin, Jean-Marc Martinez, Jiayi Cai, Pierre-Emmanuel Angeli","submitted_at":"2024-03-18T12:57:24Z","abstract_excerpt":"Several Tensor Basis Neural Network (TBNN) frameworks aimed at enhancing turbulence RANS modeling have recently been proposed in the literature as data-driven constitutive models for systems with known invariance properties. However, persistent ambiguities remain regarding the physical adequacy of applying the General Eddy Viscosity Model (GEVM). This work aims at investigating this aspect in an a priori stage for better predictions of the Reynolds stress anisotropy tensor, while preserving the Galilean and rotational invariances. In particular, we propose a general framework providing optimal"},"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":"2403.11746","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"physics.flu-dyn","submitted_at":"2024-03-18T12:57:24Z","cross_cats_sorted":["physics.comp-ph","physics.data-an"],"title_canon_sha256":"c9e4418e20805ddbc29052a18cd3d932b2ccfe37a65c534f87f644a3fdca66d9","abstract_canon_sha256":"a6cd679bcd7db09b8a017dbc14b4d282ec75efab8b2cb78d658224c64e4d12d5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:57:30.899958Z","signature_b64":"HFUziCB4mT6xmHb7JvPylBH2j0kB29YCTh/JPXOpCTiOr11Lc7/aTeZvSU1WBFslF5/IsZ8JBSNPOUQIxZzNCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a60a0f429e1952f10298d5f01dc54d2fcce7aaf5f936ea7cb22f52d9657e511","last_reissued_at":"2026-07-05T07:57:30.899559Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:57:30.899559Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Revisiting Tensor Basis Neural Networks for Reynolds stress modeling: application to plane channel and square duct flows","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["physics.comp-ph","physics.data-an"],"primary_cat":"physics.flu-dyn","authors_text":"Didier Lucor, Guillaume Damblin, Jean-Marc Martinez, Jiayi Cai, Pierre-Emmanuel Angeli","submitted_at":"2024-03-18T12:57:24Z","abstract_excerpt":"Several Tensor Basis Neural Network (TBNN) frameworks aimed at enhancing turbulence RANS modeling have recently been proposed in the literature as data-driven constitutive models for systems with known invariance properties. However, persistent ambiguities remain regarding the physical adequacy of applying the General Eddy Viscosity Model (GEVM). This work aims at investigating this aspect in an a priori stage for better predictions of the Reynolds stress anisotropy tensor, while preserving the Galilean and rotational invariances. In particular, we propose a general framework providing optimal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.11746","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/2403.11746/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":"2403.11746","created_at":"2026-07-05T07:57:30.899611+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.11746v1","created_at":"2026-07-05T07:57:30.899611+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.11746","created_at":"2026-07-05T07:57:30.899611+00:00"},{"alias_kind":"pith_short_12","alias_value":"PJQKB5BJ4GKS","created_at":"2026-07-05T07:57:30.899611+00:00"},{"alias_kind":"pith_short_16","alias_value":"PJQKB5BJ4GKS6EBJ","created_at":"2026-07-05T07:57:30.899611+00:00"},{"alias_kind":"pith_short_8","alias_value":"PJQKB5BJ","created_at":"2026-07-05T07:57:30.899611+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/PJQKB5BJ4GKS6EBJRVPQDXCU2L","json":"https://pith.science/pith/PJQKB5BJ4GKS6EBJRVPQDXCU2L.json","graph_json":"https://pith.science/api/pith-number/PJQKB5BJ4GKS6EBJRVPQDXCU2L/graph.json","events_json":"https://pith.science/api/pith-number/PJQKB5BJ4GKS6EBJRVPQDXCU2L/events.json","paper":"https://pith.science/paper/PJQKB5BJ"},"agent_actions":{"view_html":"https://pith.science/pith/PJQKB5BJ4GKS6EBJRVPQDXCU2L","download_json":"https://pith.science/pith/PJQKB5BJ4GKS6EBJRVPQDXCU2L.json","view_paper":"https://pith.science/paper/PJQKB5BJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.11746&json=true","fetch_graph":"https://pith.science/api/pith-number/PJQKB5BJ4GKS6EBJRVPQDXCU2L/graph.json","fetch_events":"https://pith.science/api/pith-number/PJQKB5BJ4GKS6EBJRVPQDXCU2L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PJQKB5BJ4GKS6EBJRVPQDXCU2L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PJQKB5BJ4GKS6EBJRVPQDXCU2L/action/storage_attestation","attest_author":"https://pith.science/pith/PJQKB5BJ4GKS6EBJRVPQDXCU2L/action/author_attestation","sign_citation":"https://pith.science/pith/PJQKB5BJ4GKS6EBJRVPQDXCU2L/action/citation_signature","submit_replication":"https://pith.science/pith/PJQKB5BJ4GKS6EBJRVPQDXCU2L/action/replication_record"}},"created_at":"2026-07-05T07:57:30.899611+00:00","updated_at":"2026-07-05T07:57:30.899611+00:00"}