{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:JT3IMATSWMWOFQPQ4CFBUVXDI2","short_pith_number":"pith:JT3IMATS","schema_version":"1.0","canonical_sha256":"4cf6860272b32ce2c1f0e08a1a56e346b76922499a8376b8d444e57f9ea54c98","source":{"kind":"arxiv","id":"2301.11848","version":1},"attestation_state":"computed","paper":{"title":"Combination of Multi-Fidelity Data Sources For Uncertainty Quantification: A Lightweight CNN Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.flu-dyn","authors_text":"Minghan Chu, Weicheng Qian","submitted_at":"2023-01-27T16:54:35Z","abstract_excerpt":"Reynolds Averaged Navier Stokes (RANS) modelling is notorious for introducing the model-form uncertainty due to the Boussinesq turbulent viscosity hypothesis. Recently, the eigenspace perturbation method (EPM) has been developed to estimate the RANS model-form uncertainty. This approach estimates model-form uncertainty through injecting perturbations to the predicted Reynolds stress tensor. However, there is a need for a reliable machine learning method for estimating the perturbed amplitude of the Reynolds stress tensor. Machine learning models are often too complex and data intensive for thi"},"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":"2301.11848","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.flu-dyn","submitted_at":"2023-01-27T16:54:35Z","cross_cats_sorted":[],"title_canon_sha256":"8a9879094651134fa148d371bc1cc5b1ea314719c86ee4d5a7addd83bc7c7652","abstract_canon_sha256":"0806615a647ec0a15e834e14e89a411abbb77304aef6a4a0bb372822cd79c422"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:36:27.074171Z","signature_b64":"p+mhtIfdgPkC2w2eYR9JMYp9YSsAN82RSPIQa9VKhjUfgK+KU/Fim0tag/HoaJ8MvAIGQw5hI9MiLcGLTX3WAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4cf6860272b32ce2c1f0e08a1a56e346b76922499a8376b8d444e57f9ea54c98","last_reissued_at":"2026-07-05T05:36:27.073634Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:36:27.073634Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Combination of Multi-Fidelity Data Sources For Uncertainty Quantification: A Lightweight CNN Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.flu-dyn","authors_text":"Minghan Chu, Weicheng Qian","submitted_at":"2023-01-27T16:54:35Z","abstract_excerpt":"Reynolds Averaged Navier Stokes (RANS) modelling is notorious for introducing the model-form uncertainty due to the Boussinesq turbulent viscosity hypothesis. Recently, the eigenspace perturbation method (EPM) has been developed to estimate the RANS model-form uncertainty. This approach estimates model-form uncertainty through injecting perturbations to the predicted Reynolds stress tensor. However, there is a need for a reliable machine learning method for estimating the perturbed amplitude of the Reynolds stress tensor. Machine learning models are often too complex and data intensive for thi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.11848","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/2301.11848/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":"2301.11848","created_at":"2026-07-05T05:36:27.073703+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.11848v1","created_at":"2026-07-05T05:36:27.073703+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.11848","created_at":"2026-07-05T05:36:27.073703+00:00"},{"alias_kind":"pith_short_12","alias_value":"JT3IMATSWMWO","created_at":"2026-07-05T05:36:27.073703+00:00"},{"alias_kind":"pith_short_16","alias_value":"JT3IMATSWMWOFQPQ","created_at":"2026-07-05T05:36:27.073703+00:00"},{"alias_kind":"pith_short_8","alias_value":"JT3IMATS","created_at":"2026-07-05T05:36:27.073703+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/JT3IMATSWMWOFQPQ4CFBUVXDI2","json":"https://pith.science/pith/JT3IMATSWMWOFQPQ4CFBUVXDI2.json","graph_json":"https://pith.science/api/pith-number/JT3IMATSWMWOFQPQ4CFBUVXDI2/graph.json","events_json":"https://pith.science/api/pith-number/JT3IMATSWMWOFQPQ4CFBUVXDI2/events.json","paper":"https://pith.science/paper/JT3IMATS"},"agent_actions":{"view_html":"https://pith.science/pith/JT3IMATSWMWOFQPQ4CFBUVXDI2","download_json":"https://pith.science/pith/JT3IMATSWMWOFQPQ4CFBUVXDI2.json","view_paper":"https://pith.science/paper/JT3IMATS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.11848&json=true","fetch_graph":"https://pith.science/api/pith-number/JT3IMATSWMWOFQPQ4CFBUVXDI2/graph.json","fetch_events":"https://pith.science/api/pith-number/JT3IMATSWMWOFQPQ4CFBUVXDI2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JT3IMATSWMWOFQPQ4CFBUVXDI2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JT3IMATSWMWOFQPQ4CFBUVXDI2/action/storage_attestation","attest_author":"https://pith.science/pith/JT3IMATSWMWOFQPQ4CFBUVXDI2/action/author_attestation","sign_citation":"https://pith.science/pith/JT3IMATSWMWOFQPQ4CFBUVXDI2/action/citation_signature","submit_replication":"https://pith.science/pith/JT3IMATSWMWOFQPQ4CFBUVXDI2/action/replication_record"}},"created_at":"2026-07-05T05:36:27.073703+00:00","updated_at":"2026-07-05T05:36:27.073703+00:00"}