{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:RGLIOJ5V3EXXOYMN26F7YUUKV2","short_pith_number":"pith:RGLIOJ5V","schema_version":"1.0","canonical_sha256":"89968727b5d92f77618dd78bfc528aaebf0f5f6029c37d32c30bc8daa4ce1231","source":{"kind":"arxiv","id":"2008.01066","version":1},"attestation_state":"computed","paper":{"title":"Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.CE","authors_text":"Guang Lin, Xiu Yang, Yixiang Deng","submitted_at":"2020-08-03T17:57:12Z","abstract_excerpt":"We propose a data fusion method based on multi-fidelity Gaussian process regression (GPR) framework. This method combines available data of the quantity of interest (QoI) and its gradients with different fidelity levels, namely, it is a Gradient-enhanced Cokriging method (GE-Cokriging). It provides the approximations of both the QoI and its gradients simultaneously with uncertainty estimates. We compare this method with the conventional multi-fidelity Cokriging method that does not use gradients information, and the result suggests that GE-Cokriging has a better performance in predicting both "},"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":"2008.01066","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CE","submitted_at":"2020-08-03T17:57:12Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"d6903b7e7a95b519afad561b0aff64d98a132e22f103d92bda88ae50ec51fc53","abstract_canon_sha256":"6179ace65232712559a7ca8d6e50e4598c84763cfcfc08270b359a304dd9dd13"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:02:42.324962Z","signature_b64":"SszjUYSGRKucmONjdWiRYgqcEFS+F3pnbCYafny+Gpu73lgjdxOlbrle/M6IXelBGzUAqoKUO6baQw32tjEHBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89968727b5d92f77618dd78bfc528aaebf0f5f6029c37d32c30bc8daa4ce1231","last_reissued_at":"2026-07-05T02:02:42.324461Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:02:42.324461Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.CE","authors_text":"Guang Lin, Xiu Yang, Yixiang Deng","submitted_at":"2020-08-03T17:57:12Z","abstract_excerpt":"We propose a data fusion method based on multi-fidelity Gaussian process regression (GPR) framework. This method combines available data of the quantity of interest (QoI) and its gradients with different fidelity levels, namely, it is a Gradient-enhanced Cokriging method (GE-Cokriging). It provides the approximations of both the QoI and its gradients simultaneously with uncertainty estimates. We compare this method with the conventional multi-fidelity Cokriging method that does not use gradients information, and the result suggests that GE-Cokriging has a better performance in predicting both "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.01066","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/2008.01066/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":"2008.01066","created_at":"2026-07-05T02:02:42.324531+00:00"},{"alias_kind":"arxiv_version","alias_value":"2008.01066v1","created_at":"2026-07-05T02:02:42.324531+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.01066","created_at":"2026-07-05T02:02:42.324531+00:00"},{"alias_kind":"pith_short_12","alias_value":"RGLIOJ5V3EXX","created_at":"2026-07-05T02:02:42.324531+00:00"},{"alias_kind":"pith_short_16","alias_value":"RGLIOJ5V3EXXOYMN","created_at":"2026-07-05T02:02:42.324531+00:00"},{"alias_kind":"pith_short_8","alias_value":"RGLIOJ5V","created_at":"2026-07-05T02:02:42.324531+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/RGLIOJ5V3EXXOYMN26F7YUUKV2","json":"https://pith.science/pith/RGLIOJ5V3EXXOYMN26F7YUUKV2.json","graph_json":"https://pith.science/api/pith-number/RGLIOJ5V3EXXOYMN26F7YUUKV2/graph.json","events_json":"https://pith.science/api/pith-number/RGLIOJ5V3EXXOYMN26F7YUUKV2/events.json","paper":"https://pith.science/paper/RGLIOJ5V"},"agent_actions":{"view_html":"https://pith.science/pith/RGLIOJ5V3EXXOYMN26F7YUUKV2","download_json":"https://pith.science/pith/RGLIOJ5V3EXXOYMN26F7YUUKV2.json","view_paper":"https://pith.science/paper/RGLIOJ5V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2008.01066&json=true","fetch_graph":"https://pith.science/api/pith-number/RGLIOJ5V3EXXOYMN26F7YUUKV2/graph.json","fetch_events":"https://pith.science/api/pith-number/RGLIOJ5V3EXXOYMN26F7YUUKV2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RGLIOJ5V3EXXOYMN26F7YUUKV2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RGLIOJ5V3EXXOYMN26F7YUUKV2/action/storage_attestation","attest_author":"https://pith.science/pith/RGLIOJ5V3EXXOYMN26F7YUUKV2/action/author_attestation","sign_citation":"https://pith.science/pith/RGLIOJ5V3EXXOYMN26F7YUUKV2/action/citation_signature","submit_replication":"https://pith.science/pith/RGLIOJ5V3EXXOYMN26F7YUUKV2/action/replication_record"}},"created_at":"2026-07-05T02:02:42.324531+00:00","updated_at":"2026-07-05T02:02:42.324531+00:00"}