{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:3SBK3MSILMPJGCIPLJKC4E3CZP","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":"a75f165d537ae62ded19379c256496e1f90e5ca967adf7fffe2de3d56c457681","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2018-06-13T02:56:09Z","title_canon_sha256":"82260b6b793dc3325e8fd90a1ca6507bc759a9b490f9e98f3ff6150406f4f184"},"schema_version":"1.0","source":{"id":"1806.04830","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.04830","created_at":"2026-05-18T00:13:20Z"},{"alias_kind":"arxiv_version","alias_value":"1806.04830v1","created_at":"2026-05-18T00:13:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.04830","created_at":"2026-05-18T00:13:20Z"},{"alias_kind":"pith_short_12","alias_value":"3SBK3MSILMPJ","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_16","alias_value":"3SBK3MSILMPJGCIP","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_8","alias_value":"3SBK3MSI","created_at":"2026-05-18T12:32:05Z"}],"graph_snapshots":[{"event_id":"sha256:e22f217b74f388d89d4aace7240991bc4fe2f2c509046aab9298560647c028b5","target":"graph","created_at":"2026-05-18T00:13:20Z","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":"The objective of this paper is to design novel multi-layer neural network architectures for multiscale simulations of flows taking into account the observed data and physical modeling concepts. Our approaches use deep learning concepts combined with local multiscale model reduction methodologies to predict flow dynamics. Using reduced-order model concepts is important for constructing robust deep learning architectures since the reduced-order models provide fewer degrees of freedom. Flow dynamics can be thought of as multi-layer networks. More precisely, the solution (e.g., pressures and satur","authors_text":"Eric T. Chung, Min Wang, Siu Wun Cheung, Yalchin Efendiev, Yating Wang","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2018-06-13T02:56:09Z","title":"Deep Multiscale Model Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.04830","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":"sha256:4386ae1134f94c5de208fd7997883f7022d0575d75221ef6d729fdcb8e25bec0","target":"record","created_at":"2026-05-18T00:13:20Z","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":"a75f165d537ae62ded19379c256496e1f90e5ca967adf7fffe2de3d56c457681","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2018-06-13T02:56:09Z","title_canon_sha256":"82260b6b793dc3325e8fd90a1ca6507bc759a9b490f9e98f3ff6150406f4f184"},"schema_version":"1.0","source":{"id":"1806.04830","kind":"arxiv","version":1}},"canonical_sha256":"dc82adb2485b1e93090f5a542e1362cbc48acd8238034cd908523724125c24e5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dc82adb2485b1e93090f5a542e1362cbc48acd8238034cd908523724125c24e5","first_computed_at":"2026-05-18T00:13:20.763143Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:13:20.763143Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DDVnZG/Ejs6y4qXx7vraZ3F1AtWB74L+RpMx+IFe6tZNc3lVixZd79fouaIwrW/CgydMfiMWIKMJmlkA5y4XCA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:13:20.763724Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.04830","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4386ae1134f94c5de208fd7997883f7022d0575d75221ef6d729fdcb8e25bec0","sha256:e22f217b74f388d89d4aace7240991bc4fe2f2c509046aab9298560647c028b5"],"state_sha256":"a2d8fb0f69fec56d8f696503461833ed27d8d964b214c96a2ed991c1d572a233"}