{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:IZFSMO2QJN7R4DAC76BSB5HLMG","short_pith_number":"pith:IZFSMO2Q","canonical_record":{"source":{"id":"1806.05832","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2018-06-15T07:22:33Z","cross_cats_sorted":[],"title_canon_sha256":"8336f1ede47cdcc1351e545d9a954b68e9076ae97b0ba5536626b24fe8858f28","abstract_canon_sha256":"dc7a0080430481bd848c3f8206b48a69cb00bff4fe78aaae09bed636129af7f5"},"schema_version":"1.0"},"canonical_sha256":"464b263b504b7f1e0c02ff8320f4eb61915318ab11cff1fb8340aa8d72f5d2fe","source":{"kind":"arxiv","id":"1806.05832","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.05832","created_at":"2026-05-18T00:13:09Z"},{"alias_kind":"arxiv_version","alias_value":"1806.05832v1","created_at":"2026-05-18T00:13:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.05832","created_at":"2026-05-18T00:13:09Z"},{"alias_kind":"pith_short_12","alias_value":"IZFSMO2QJN7R","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"IZFSMO2QJN7R4DAC","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"IZFSMO2Q","created_at":"2026-05-18T12:32:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:IZFSMO2QJN7R4DAC76BSB5HLMG","target":"record","payload":{"canonical_record":{"source":{"id":"1806.05832","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2018-06-15T07:22:33Z","cross_cats_sorted":[],"title_canon_sha256":"8336f1ede47cdcc1351e545d9a954b68e9076ae97b0ba5536626b24fe8858f28","abstract_canon_sha256":"dc7a0080430481bd848c3f8206b48a69cb00bff4fe78aaae09bed636129af7f5"},"schema_version":"1.0"},"canonical_sha256":"464b263b504b7f1e0c02ff8320f4eb61915318ab11cff1fb8340aa8d72f5d2fe","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:13:09.310998Z","signature_b64":"/hNSzehwov7yamZn0jbt77L5ILctYs0qZWqI7J/dBBV4OufII33sFP865aKsX9s74Rs95GwK0vdxQPpiqtI7Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"464b263b504b7f1e0c02ff8320f4eb61915318ab11cff1fb8340aa8d72f5d2fe","last_reissued_at":"2026-05-18T00:13:09.310245Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:13:09.310245Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.05832","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:13:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6mPvLNJ8Wg30QdfYWyP8Q9zKwBtiUDNjTUycZufXOb1n85BiTHZwL75sz9D2nAxOxo5NWiAVswMjfQ70MkdOBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T12:05:40.509684Z"},"content_sha256":"94b2702a4f9b84201ab06964a54f6b8391880387af83695ce7c3a295034d0c6f","schema_version":"1.0","event_id":"sha256:94b2702a4f9b84201ab06964a54f6b8391880387af83695ce7c3a295034d0c6f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:IZFSMO2QJN7R4DAC76BSB5HLMG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Dynamic Data-driven Bayesian GMsFEM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.NA","authors_text":"Nilabja Guha, Siu Wun Cheung","submitted_at":"2018-06-15T07:22:33Z","abstract_excerpt":"In this paper, we propose a Bayesian approach for multiscale problems with the availability of dynamic observational data. Our method selects important degrees of freedom probabilistically in a Generalized multiscale finite element method framework. Due to scale disparity in many multiscale applications, computational models can not resolve all scales. Dominant modes in the Generalized Multiscale Finite Element Method are used as \"permanent\" basis functions, which we use to compute an inexpensive multiscale solution and the associated uncertainties. Through our Bayesian framework, we can model"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.05832","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:13:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nfV+j81HztvDrdICtd2GMfbRdk+pY0AFQsoXr0WRHeQDTDBNWHldk7LrNr0Qc5d8FB5IQ+yUYrQZ3c2WuyVGAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T12:05:40.510265Z"},"content_sha256":"6060210089c11864a16ebac6628f221e23a88132a15d2bda783a44be5b9091ea","schema_version":"1.0","event_id":"sha256:6060210089c11864a16ebac6628f221e23a88132a15d2bda783a44be5b9091ea"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IZFSMO2QJN7R4DAC76BSB5HLMG/bundle.json","state_url":"https://pith.science/pith/IZFSMO2QJN7R4DAC76BSB5HLMG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IZFSMO2QJN7R4DAC76BSB5HLMG/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":"2026-05-30T12:05:40Z","links":{"resolver":"https://pith.science/pith/IZFSMO2QJN7R4DAC76BSB5HLMG","bundle":"https://pith.science/pith/IZFSMO2QJN7R4DAC76BSB5HLMG/bundle.json","state":"https://pith.science/pith/IZFSMO2QJN7R4DAC76BSB5HLMG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IZFSMO2QJN7R4DAC76BSB5HLMG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:IZFSMO2QJN7R4DAC76BSB5HLMG","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":"dc7a0080430481bd848c3f8206b48a69cb00bff4fe78aaae09bed636129af7f5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2018-06-15T07:22:33Z","title_canon_sha256":"8336f1ede47cdcc1351e545d9a954b68e9076ae97b0ba5536626b24fe8858f28"},"schema_version":"1.0","source":{"id":"1806.05832","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.05832","created_at":"2026-05-18T00:13:09Z"},{"alias_kind":"arxiv_version","alias_value":"1806.05832v1","created_at":"2026-05-18T00:13:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.05832","created_at":"2026-05-18T00:13:09Z"},{"alias_kind":"pith_short_12","alias_value":"IZFSMO2QJN7R","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"IZFSMO2QJN7R4DAC","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"IZFSMO2Q","created_at":"2026-05-18T12:32:31Z"}],"graph_snapshots":[{"event_id":"sha256:6060210089c11864a16ebac6628f221e23a88132a15d2bda783a44be5b9091ea","target":"graph","created_at":"2026-05-18T00:13:09Z","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"},"paper":{"abstract_excerpt":"In this paper, we propose a Bayesian approach for multiscale problems with the availability of dynamic observational data. Our method selects important degrees of freedom probabilistically in a Generalized multiscale finite element method framework. Due to scale disparity in many multiscale applications, computational models can not resolve all scales. Dominant modes in the Generalized Multiscale Finite Element Method are used as \"permanent\" basis functions, which we use to compute an inexpensive multiscale solution and the associated uncertainties. Through our Bayesian framework, we can model","authors_text":"Nilabja Guha, Siu Wun Cheung","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2018-06-15T07:22:33Z","title":"Dynamic Data-driven Bayesian GMsFEM"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.05832","kind":"arxiv","version":1},"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":"sha256:94b2702a4f9b84201ab06964a54f6b8391880387af83695ce7c3a295034d0c6f","target":"record","created_at":"2026-05-18T00:13:09Z","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":"dc7a0080430481bd848c3f8206b48a69cb00bff4fe78aaae09bed636129af7f5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2018-06-15T07:22:33Z","title_canon_sha256":"8336f1ede47cdcc1351e545d9a954b68e9076ae97b0ba5536626b24fe8858f28"},"schema_version":"1.0","source":{"id":"1806.05832","kind":"arxiv","version":1}},"canonical_sha256":"464b263b504b7f1e0c02ff8320f4eb61915318ab11cff1fb8340aa8d72f5d2fe","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"464b263b504b7f1e0c02ff8320f4eb61915318ab11cff1fb8340aa8d72f5d2fe","first_computed_at":"2026-05-18T00:13:09.310245Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:13:09.310245Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/hNSzehwov7yamZn0jbt77L5ILctYs0qZWqI7J/dBBV4OufII33sFP865aKsX9s74Rs95GwK0vdxQPpiqtI7Bg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:13:09.310998Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.05832","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:94b2702a4f9b84201ab06964a54f6b8391880387af83695ce7c3a295034d0c6f","sha256:6060210089c11864a16ebac6628f221e23a88132a15d2bda783a44be5b9091ea"],"state_sha256":"1e8a961d699ef0d7718bad71389f5e44dc5a9ef0cad50fb36b08217c3e5da9ec"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DLqQligs8DHMYh+oZ6jQOrsAdex6slhpWRWUNQ4DdhsXJBg6qC7b4cK6TGlfx0NqV1VL6un6yFsec9akNMjTCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T12:05:40.513156Z","bundle_sha256":"2515da117c117c948322f31f92e2c3ee29c712e5403c3926dd1735fb49c0105c"}}