{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:NUDLH63PSIKTRH5I3RK5BY62XV","short_pith_number":"pith:NUDLH63P","schema_version":"1.0","canonical_sha256":"6d06b3fb6f9215389fa8dc55d0e3dabd58c2097e40e2eb8a5d5ab5c239463db3","source":{"kind":"arxiv","id":"2512.18066","version":2},"attestation_state":"computed","paper":{"title":"Gradient-enhancement and Gradient Predictions for Deep Gaussian Process Modeling of Expensive Computer Experiments","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Annie S. Booth","submitted_at":"2025-12-19T21:10:03Z","abstract_excerpt":"Deep Gaussian processes (DGPs) are popular surrogate models for complex nonstationary computer experiments. DGPs use one or more latent Gaussian processes (GPs) to warp the input space into a plausibly stationary regime, then use typical GP regression on the warped domain. While this composition of GPs is conceptually straightforward, the functional nature of the multi-dimensional latent warping makes Bayesian posterior inference challenging. Traditional GPs with smooth kernels are naturally suited for the integration of gradient information, but the integration of gradients within a DGP prese"},"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":"2512.18066","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2025-12-19T21:10:03Z","cross_cats_sorted":[],"title_canon_sha256":"b0bab9cd8871c71f105fcb4e9317d5df9ad86a9b60ecea4dc81f98d6a9861b88","abstract_canon_sha256":"dc46ed718cdf79629278b8db4a641b0f05c0c2deef4f9d55c1e1bc503cb56615"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-24T00:23:06.604311Z","signature_b64":"La3jhW84zNWuKpWSn4X5SEt2kcF8FIQqtF8IQAxyAoHhnrdKTOc/dAYl+Sc11XW9i4GJSrpP4daSCDFhf4/gBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6d06b3fb6f9215389fa8dc55d0e3dabd58c2097e40e2eb8a5d5ab5c239463db3","last_reissued_at":"2026-07-24T00:23:06.603357Z","signature_status":"signed_v1","first_computed_at":"2026-07-24T00:23:06.603357Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Gradient-enhancement and Gradient Predictions for Deep Gaussian Process Modeling of Expensive Computer Experiments","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Annie S. Booth","submitted_at":"2025-12-19T21:10:03Z","abstract_excerpt":"Deep Gaussian processes (DGPs) are popular surrogate models for complex nonstationary computer experiments. DGPs use one or more latent Gaussian processes (GPs) to warp the input space into a plausibly stationary regime, then use typical GP regression on the warped domain. While this composition of GPs is conceptually straightforward, the functional nature of the multi-dimensional latent warping makes Bayesian posterior inference challenging. Traditional GPs with smooth kernels are naturally suited for the integration of gradient information, but the integration of gradients within a DGP prese"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2512.18066","kind":"arxiv","version":2},"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/2512.18066/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":"2512.18066","created_at":"2026-07-24T00:23:06.603803+00:00"},{"alias_kind":"arxiv_version","alias_value":"2512.18066v2","created_at":"2026-07-24T00:23:06.603803+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2512.18066","created_at":"2026-07-24T00:23:06.603803+00:00"},{"alias_kind":"pith_short_12","alias_value":"NUDLH63PSIKT","created_at":"2026-07-24T00:23:06.603803+00:00"},{"alias_kind":"pith_short_16","alias_value":"NUDLH63PSIKTRH5I","created_at":"2026-07-24T00:23:06.603803+00:00"},{"alias_kind":"pith_short_8","alias_value":"NUDLH63P","created_at":"2026-07-24T00:23:06.603803+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/NUDLH63PSIKTRH5I3RK5BY62XV","json":"https://pith.science/pith/NUDLH63PSIKTRH5I3RK5BY62XV.json","graph_json":"https://pith.science/api/pith-number/NUDLH63PSIKTRH5I3RK5BY62XV/graph.json","events_json":"https://pith.science/api/pith-number/NUDLH63PSIKTRH5I3RK5BY62XV/events.json","paper":"https://pith.science/paper/NUDLH63P"},"agent_actions":{"view_html":"https://pith.science/pith/NUDLH63PSIKTRH5I3RK5BY62XV","download_json":"https://pith.science/pith/NUDLH63PSIKTRH5I3RK5BY62XV.json","view_paper":"https://pith.science/paper/NUDLH63P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2512.18066&json=true","fetch_graph":"https://pith.science/api/pith-number/NUDLH63PSIKTRH5I3RK5BY62XV/graph.json","fetch_events":"https://pith.science/api/pith-number/NUDLH63PSIKTRH5I3RK5BY62XV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NUDLH63PSIKTRH5I3RK5BY62XV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NUDLH63PSIKTRH5I3RK5BY62XV/action/storage_attestation","attest_author":"https://pith.science/pith/NUDLH63PSIKTRH5I3RK5BY62XV/action/author_attestation","sign_citation":"https://pith.science/pith/NUDLH63PSIKTRH5I3RK5BY62XV/action/citation_signature","submit_replication":"https://pith.science/pith/NUDLH63PSIKTRH5I3RK5BY62XV/action/replication_record"}},"created_at":"2026-07-24T00:23:06.603803+00:00","updated_at":"2026-07-24T00:23:06.603803+00:00"}