{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:AK4DZUG2G6QCRVLLQMS2SLIXLD","short_pith_number":"pith:AK4DZUG2","schema_version":"1.0","canonical_sha256":"02b83cd0da37a028d56b8325a92d1758f17fec3abee2d4a9b090316c801f35f5","source":{"kind":"arxiv","id":"1801.06879","version":1},"attestation_state":"computed","paper":{"title":"Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","stat.ML"],"primary_cat":"physics.comp-ph","authors_text":"Nicholas Zabaras, Yinhao Zhu","submitted_at":"2018-01-21T19:18:13Z","abstract_excerpt":"We are interested in the development of surrogate models for uncertainty quantification and propagation in problems governed by stochastic PDEs using a deep convolutional encoder-decoder network in a similar fashion to approaches considered in deep learning for image-to-image regression tasks. Since normal neural networks are data intensive and cannot provide predictive uncertainty, we propose a Bayesian approach to convolutional neural nets. A recently introduced variational gradient descent algorithm based on Stein's method is scaled to deep convolutional networks to perform approximate Baye"},"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":"1801.06879","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2018-01-21T19:18:13Z","cross_cats_sorted":["cs.CV","cs.LG","stat.ML"],"title_canon_sha256":"7603e5599874fc6e9fcdab9b3c1c91d26c7561cf8b7fbb3c033fe7025f635c72","abstract_canon_sha256":"aa8bc98a321cc58102579661a0c43859291c91f987691bfd482b533df8b926eb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:16:26.964800Z","signature_b64":"e81FSr3EIr4elB9av5lOrCDe0GIJgR5xRdLd+QpmwDXsC+Cvh6XnyNc61iZa9EaDIqJmjPlJg4Ju6wRf4mJxDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"02b83cd0da37a028d56b8325a92d1758f17fec3abee2d4a9b090316c801f35f5","last_reissued_at":"2026-05-18T00:16:26.964156Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:16:26.964156Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","stat.ML"],"primary_cat":"physics.comp-ph","authors_text":"Nicholas Zabaras, Yinhao Zhu","submitted_at":"2018-01-21T19:18:13Z","abstract_excerpt":"We are interested in the development of surrogate models for uncertainty quantification and propagation in problems governed by stochastic PDEs using a deep convolutional encoder-decoder network in a similar fashion to approaches considered in deep learning for image-to-image regression tasks. Since normal neural networks are data intensive and cannot provide predictive uncertainty, we propose a Bayesian approach to convolutional neural nets. A recently introduced variational gradient descent algorithm based on Stein's method is scaled to deep convolutional networks to perform approximate Baye"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.06879","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":""},"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":"1801.06879","created_at":"2026-05-18T00:16:26.964251+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.06879v1","created_at":"2026-05-18T00:16:26.964251+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.06879","created_at":"2026-05-18T00:16:26.964251+00:00"},{"alias_kind":"pith_short_12","alias_value":"AK4DZUG2G6QC","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_16","alias_value":"AK4DZUG2G6QCRVLL","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_8","alias_value":"AK4DZUG2","created_at":"2026-05-18T12:32:13.499390+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/AK4DZUG2G6QCRVLLQMS2SLIXLD","json":"https://pith.science/pith/AK4DZUG2G6QCRVLLQMS2SLIXLD.json","graph_json":"https://pith.science/api/pith-number/AK4DZUG2G6QCRVLLQMS2SLIXLD/graph.json","events_json":"https://pith.science/api/pith-number/AK4DZUG2G6QCRVLLQMS2SLIXLD/events.json","paper":"https://pith.science/paper/AK4DZUG2"},"agent_actions":{"view_html":"https://pith.science/pith/AK4DZUG2G6QCRVLLQMS2SLIXLD","download_json":"https://pith.science/pith/AK4DZUG2G6QCRVLLQMS2SLIXLD.json","view_paper":"https://pith.science/paper/AK4DZUG2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.06879&json=true","fetch_graph":"https://pith.science/api/pith-number/AK4DZUG2G6QCRVLLQMS2SLIXLD/graph.json","fetch_events":"https://pith.science/api/pith-number/AK4DZUG2G6QCRVLLQMS2SLIXLD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AK4DZUG2G6QCRVLLQMS2SLIXLD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AK4DZUG2G6QCRVLLQMS2SLIXLD/action/storage_attestation","attest_author":"https://pith.science/pith/AK4DZUG2G6QCRVLLQMS2SLIXLD/action/author_attestation","sign_citation":"https://pith.science/pith/AK4DZUG2G6QCRVLLQMS2SLIXLD/action/citation_signature","submit_replication":"https://pith.science/pith/AK4DZUG2G6QCRVLLQMS2SLIXLD/action/replication_record"}},"created_at":"2026-05-18T00:16:26.964251+00:00","updated_at":"2026-05-18T00:16:26.964251+00:00"}