{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:VZNI6WHYMWVAH64J4D2IGRWCQI","short_pith_number":"pith:VZNI6WHY","canonical_record":{"source":{"id":"2301.05708","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ML","submitted_at":"2023-01-09T07:35:43Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"63c413108156a9714ac9c568e514a90aca3cc1d5b563a67d4c44394ef3403f40","abstract_canon_sha256":"e6e0e164c303a19cadeac38373d6d9e24577d7fbd4ea85736c0b6b8d04e25fe0"},"schema_version":"1.0"},"canonical_sha256":"ae5a8f58f865aa03fb89e0f48346c2823ac3533dc9fe0c08286bd076d300fba2","source":{"kind":"arxiv","id":"2301.05708","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.05708","created_at":"2026-07-05T05:39:30Z"},{"alias_kind":"arxiv_version","alias_value":"2301.05708v2","created_at":"2026-07-05T05:39:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.05708","created_at":"2026-07-05T05:39:30Z"},{"alias_kind":"pith_short_12","alias_value":"VZNI6WHYMWVA","created_at":"2026-07-05T05:39:30Z"},{"alias_kind":"pith_short_16","alias_value":"VZNI6WHYMWVAH64J","created_at":"2026-07-05T05:39:30Z"},{"alias_kind":"pith_short_8","alias_value":"VZNI6WHY","created_at":"2026-07-05T05:39:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:VZNI6WHYMWVAH64J4D2IGRWCQI","target":"record","payload":{"canonical_record":{"source":{"id":"2301.05708","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ML","submitted_at":"2023-01-09T07:35:43Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"63c413108156a9714ac9c568e514a90aca3cc1d5b563a67d4c44394ef3403f40","abstract_canon_sha256":"e6e0e164c303a19cadeac38373d6d9e24577d7fbd4ea85736c0b6b8d04e25fe0"},"schema_version":"1.0"},"canonical_sha256":"ae5a8f58f865aa03fb89e0f48346c2823ac3533dc9fe0c08286bd076d300fba2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:39:30.602260Z","signature_b64":"3x10w8W4wDQzGKTrszOnPtmI3fLhXDPVExNKL9YtYPTK2njgeQSLAjZ9FcucG5ZlbX6gzJMtISnJglo2bsGMBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae5a8f58f865aa03fb89e0f48346c2823ac3533dc9fe0c08286bd076d300fba2","last_reissued_at":"2026-07-05T05:39:30.601834Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:39:30.601834Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2301.05708","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:39:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5HIYnYyLOfAQGC5njmO5zqEVX2EINCdgeNoIpUAntxStTe3Uc51BohbCjOe5X/Fw/o8CRQOtAZ7bikSb26z6Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T06:58:08.348514Z"},"content_sha256":"8c479dcb5d4d6f43eaf65de6bddb0dcf2eaba1f85ebe506d030e35be3080a2cc","schema_version":"1.0","event_id":"sha256:8c479dcb5d4d6f43eaf65de6bddb0dcf2eaba1f85ebe506d030e35be3080a2cc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:VZNI6WHYMWVAH64J4D2IGRWCQI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A domain-decomposed VAE method for Bayesian inverse problems","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Qifeng Liao, Yingzhi Xia, Zhihang Xu","submitted_at":"2023-01-09T07:35:43Z","abstract_excerpt":"Bayesian inverse problems are often computationally challenging when the forward model is governed by complex partial differential equations (PDEs). This is typically caused by expensive forward model evaluations and high-dimensional parameterization of priors. This paper proposes a domain-decomposed variational auto-encoder Markov chain Monte Carlo (DD-VAE-MCMC) method to tackle these challenges simultaneously. Through partitioning the global physical domain into small subdomains, the proposed method first constructs local deterministic generative models based on local historical data, which "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.05708","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/2301.05708/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:39:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Thn/rlg/YACqmbl9BW20/oOiFbeRudzhtlRfW1kERuDvx5nva0Or7fnJb9HSD0A4uCKIjP16ApEtYrYQh8n1DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T06:58:08.349618Z"},"content_sha256":"13280f78819ee078ac71bd4f9ff4fb5176ca783551058a9dda86286754dd5c58","schema_version":"1.0","event_id":"sha256:13280f78819ee078ac71bd4f9ff4fb5176ca783551058a9dda86286754dd5c58"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VZNI6WHYMWVAH64J4D2IGRWCQI/bundle.json","state_url":"https://pith.science/pith/VZNI6WHYMWVAH64J4D2IGRWCQI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VZNI6WHYMWVAH64J4D2IGRWCQI/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-04T06:58:08Z","links":{"resolver":"https://pith.science/pith/VZNI6WHYMWVAH64J4D2IGRWCQI","bundle":"https://pith.science/pith/VZNI6WHYMWVAH64J4D2IGRWCQI/bundle.json","state":"https://pith.science/pith/VZNI6WHYMWVAH64J4D2IGRWCQI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VZNI6WHYMWVAH64J4D2IGRWCQI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:VZNI6WHYMWVAH64J4D2IGRWCQI","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e6e0e164c303a19cadeac38373d6d9e24577d7fbd4ea85736c0b6b8d04e25fe0","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ML","submitted_at":"2023-01-09T07:35:43Z","title_canon_sha256":"63c413108156a9714ac9c568e514a90aca3cc1d5b563a67d4c44394ef3403f40"},"schema_version":"1.0","source":{"id":"2301.05708","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.05708","created_at":"2026-07-05T05:39:30Z"},{"alias_kind":"arxiv_version","alias_value":"2301.05708v2","created_at":"2026-07-05T05:39:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.05708","created_at":"2026-07-05T05:39:30Z"},{"alias_kind":"pith_short_12","alias_value":"VZNI6WHYMWVA","created_at":"2026-07-05T05:39:30Z"},{"alias_kind":"pith_short_16","alias_value":"VZNI6WHYMWVAH64J","created_at":"2026-07-05T05:39:30Z"},{"alias_kind":"pith_short_8","alias_value":"VZNI6WHY","created_at":"2026-07-05T05:39:30Z"}],"graph_snapshots":[{"event_id":"sha256:13280f78819ee078ac71bd4f9ff4fb5176ca783551058a9dda86286754dd5c58","target":"graph","created_at":"2026-07-05T05:39:30Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2301.05708/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Bayesian inverse problems are often computationally challenging when the forward model is governed by complex partial differential equations (PDEs). This is typically caused by expensive forward model evaluations and high-dimensional parameterization of priors. This paper proposes a domain-decomposed variational auto-encoder Markov chain Monte Carlo (DD-VAE-MCMC) method to tackle these challenges simultaneously. Through partitioning the global physical domain into small subdomains, the proposed method first constructs local deterministic generative models based on local historical data, which ","authors_text":"Qifeng Liao, Yingzhi Xia, Zhihang Xu","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ML","submitted_at":"2023-01-09T07:35:43Z","title":"A domain-decomposed VAE method for Bayesian inverse problems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.05708","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:8c479dcb5d4d6f43eaf65de6bddb0dcf2eaba1f85ebe506d030e35be3080a2cc","target":"record","created_at":"2026-07-05T05:39:30Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e6e0e164c303a19cadeac38373d6d9e24577d7fbd4ea85736c0b6b8d04e25fe0","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ML","submitted_at":"2023-01-09T07:35:43Z","title_canon_sha256":"63c413108156a9714ac9c568e514a90aca3cc1d5b563a67d4c44394ef3403f40"},"schema_version":"1.0","source":{"id":"2301.05708","kind":"arxiv","version":2}},"canonical_sha256":"ae5a8f58f865aa03fb89e0f48346c2823ac3533dc9fe0c08286bd076d300fba2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ae5a8f58f865aa03fb89e0f48346c2823ac3533dc9fe0c08286bd076d300fba2","first_computed_at":"2026-07-05T05:39:30.601834Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:39:30.601834Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3x10w8W4wDQzGKTrszOnPtmI3fLhXDPVExNKL9YtYPTK2njgeQSLAjZ9FcucG5ZlbX6gzJMtISnJglo2bsGMBg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:39:30.602260Z","signed_message":"canonical_sha256_bytes"},"source_id":"2301.05708","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8c479dcb5d4d6f43eaf65de6bddb0dcf2eaba1f85ebe506d030e35be3080a2cc","sha256:13280f78819ee078ac71bd4f9ff4fb5176ca783551058a9dda86286754dd5c58"],"state_sha256":"5f9e1755a79dc2c3239144f78a1e333d006fa1f31b12cd3f564f586ae751a96f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FAhm+fhZ82dWEPNHxgI4EnrLD8IYlBk7WuaSkcyg5CaZBfGW9XBwlqpMiR7IuwgaWIoO4DJatYjAaVNJm+NABQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T06:58:08.356613Z","bundle_sha256":"dbba08863f2e5a9b40b0b6ff4068809f4d626edb9fcec225d9b9714097c2e4c2"}}