{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:ZYQW4FZ6Z3U322GXHAVNO3MGXI","short_pith_number":"pith:ZYQW4FZ6","canonical_record":{"source":{"id":"1809.02996","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2018-09-09T16:01:16Z","cross_cats_sorted":[],"title_canon_sha256":"ea1c02a83f3539381dc00caea65735e32af1c065351c967b1611a8d6589ee167","abstract_canon_sha256":"b8d4ba04558a3aabccd96dfff51776f33d154ba7ca8e61f03a8597aa9ab5c750"},"schema_version":"1.0"},"canonical_sha256":"ce216e173ecee9bd68d7382ad76d86ba0cad27cd48b6082d12f4559d0a196c04","source":{"kind":"arxiv","id":"1809.02996","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.02996","created_at":"2026-05-18T00:06:10Z"},{"alias_kind":"arxiv_version","alias_value":"1809.02996v1","created_at":"2026-05-18T00:06:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.02996","created_at":"2026-05-18T00:06:10Z"},{"alias_kind":"pith_short_12","alias_value":"ZYQW4FZ6Z3U3","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"ZYQW4FZ6Z3U322GX","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"ZYQW4FZ6","created_at":"2026-05-18T12:33:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:ZYQW4FZ6Z3U322GXHAVNO3MGXI","target":"record","payload":{"canonical_record":{"source":{"id":"1809.02996","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2018-09-09T16:01:16Z","cross_cats_sorted":[],"title_canon_sha256":"ea1c02a83f3539381dc00caea65735e32af1c065351c967b1611a8d6589ee167","abstract_canon_sha256":"b8d4ba04558a3aabccd96dfff51776f33d154ba7ca8e61f03a8597aa9ab5c750"},"schema_version":"1.0"},"canonical_sha256":"ce216e173ecee9bd68d7382ad76d86ba0cad27cd48b6082d12f4559d0a196c04","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:10.389403Z","signature_b64":"MSGxhZ4vmnO8BDjnkzfxOFPRJ0TieO8oZt5dmMNar4cE+9iI8ViYiLlwRpV0rLJ/l4EzwDVMSH2UiA8LhRiCBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ce216e173ecee9bd68d7382ad76d86ba0cad27cd48b6082d12f4559d0a196c04","last_reissued_at":"2026-05-18T00:06:10.388462Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:10.388462Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.02996","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":"2026-05-18T00:06:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vc8gHyfykn9LdMyjeVgsT50xqw7G4iE8aaNkY1uI7+XMmeqDkRceciKcve0TD23aTxZC+vYsGnqd/Ge31g0kAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T06:19:43.949602Z"},"content_sha256":"558a4e13212abdf829e5481e15be8c1696e3c6e105089b64cd46d530f3067618","schema_version":"1.0","event_id":"sha256:558a4e13212abdf829e5481e15be8c1696e3c6e105089b64cd46d530f3067618"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:ZYQW4FZ6Z3U322GXHAVNO3MGXI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Seeing Permeability From Images: Fast Prediction with Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.comp-ph","authors_text":"Heng Xiao, Jin-Long Wu, Xiao-Long Yin","submitted_at":"2018-09-09T16:01:16Z","abstract_excerpt":"Fast prediction of permeability directly from images enabled by image recognition neural networks is a novel pore-scale modeling method that has a great potential. This article presents a framework that includes (1) generation of porous media samples, (2) computation of permeability via fluid dynamics simulations, (3) training of convolutional neural networks (CNN) with simulated data, and (4) validations against simulations. Comparison of machine learning results and the ground truths suggests excellent predictive performance across a wide range of porosities and pore geometries, especially f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.02996","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":"2026-05-18T00:06:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P7q3XyU2TfcUKv5zgH7UHXSPAt+/bDLMUIi1tItSqyOMQT7PNBdX7HIPsOlEd/+a9o63kLVdavKolCZObCahBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T06:19:43.950192Z"},"content_sha256":"1b5e6bc3f97956df2393e2f4301e62c6db36d8240815f20f9cd69647cc33fe8c","schema_version":"1.0","event_id":"sha256:1b5e6bc3f97956df2393e2f4301e62c6db36d8240815f20f9cd69647cc33fe8c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZYQW4FZ6Z3U322GXHAVNO3MGXI/bundle.json","state_url":"https://pith.science/pith/ZYQW4FZ6Z3U322GXHAVNO3MGXI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZYQW4FZ6Z3U322GXHAVNO3MGXI/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":"2026-06-07T06:19:43Z","links":{"resolver":"https://pith.science/pith/ZYQW4FZ6Z3U322GXHAVNO3MGXI","bundle":"https://pith.science/pith/ZYQW4FZ6Z3U322GXHAVNO3MGXI/bundle.json","state":"https://pith.science/pith/ZYQW4FZ6Z3U322GXHAVNO3MGXI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZYQW4FZ6Z3U322GXHAVNO3MGXI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:ZYQW4FZ6Z3U322GXHAVNO3MGXI","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":"b8d4ba04558a3aabccd96dfff51776f33d154ba7ca8e61f03a8597aa9ab5c750","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2018-09-09T16:01:16Z","title_canon_sha256":"ea1c02a83f3539381dc00caea65735e32af1c065351c967b1611a8d6589ee167"},"schema_version":"1.0","source":{"id":"1809.02996","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.02996","created_at":"2026-05-18T00:06:10Z"},{"alias_kind":"arxiv_version","alias_value":"1809.02996v1","created_at":"2026-05-18T00:06:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.02996","created_at":"2026-05-18T00:06:10Z"},{"alias_kind":"pith_short_12","alias_value":"ZYQW4FZ6Z3U3","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"ZYQW4FZ6Z3U322GX","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"ZYQW4FZ6","created_at":"2026-05-18T12:33:07Z"}],"graph_snapshots":[{"event_id":"sha256:1b5e6bc3f97956df2393e2f4301e62c6db36d8240815f20f9cd69647cc33fe8c","target":"graph","created_at":"2026-05-18T00:06:10Z","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":"Fast prediction of permeability directly from images enabled by image recognition neural networks is a novel pore-scale modeling method that has a great potential. This article presents a framework that includes (1) generation of porous media samples, (2) computation of permeability via fluid dynamics simulations, (3) training of convolutional neural networks (CNN) with simulated data, and (4) validations against simulations. Comparison of machine learning results and the ground truths suggests excellent predictive performance across a wide range of porosities and pore geometries, especially f","authors_text":"Heng Xiao, Jin-Long Wu, Xiao-Long Yin","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2018-09-09T16:01:16Z","title":"Seeing Permeability From Images: Fast Prediction with Convolutional Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.02996","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":"sha256:558a4e13212abdf829e5481e15be8c1696e3c6e105089b64cd46d530f3067618","target":"record","created_at":"2026-05-18T00:06:10Z","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":"b8d4ba04558a3aabccd96dfff51776f33d154ba7ca8e61f03a8597aa9ab5c750","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2018-09-09T16:01:16Z","title_canon_sha256":"ea1c02a83f3539381dc00caea65735e32af1c065351c967b1611a8d6589ee167"},"schema_version":"1.0","source":{"id":"1809.02996","kind":"arxiv","version":1}},"canonical_sha256":"ce216e173ecee9bd68d7382ad76d86ba0cad27cd48b6082d12f4559d0a196c04","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ce216e173ecee9bd68d7382ad76d86ba0cad27cd48b6082d12f4559d0a196c04","first_computed_at":"2026-05-18T00:06:10.388462Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:06:10.388462Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MSGxhZ4vmnO8BDjnkzfxOFPRJ0TieO8oZt5dmMNar4cE+9iI8ViYiLlwRpV0rLJ/l4EzwDVMSH2UiA8LhRiCBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:06:10.389403Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.02996","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:558a4e13212abdf829e5481e15be8c1696e3c6e105089b64cd46d530f3067618","sha256:1b5e6bc3f97956df2393e2f4301e62c6db36d8240815f20f9cd69647cc33fe8c"],"state_sha256":"e5572bea5726dabbf669ac5947a2a07674bc4d911f99b40efc8e688aa7601fdf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"B1UrR53IRCHEAU1yF+wzUENbIL38av0g8FNPNuzKutsNLDLbZAFBeZ3sYgUbN1krT/y2dAENq/iR0WzSNbMTAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T06:19:43.953237Z","bundle_sha256":"17199f12c7cb0346e64fb842ed0d4ac5585513d6e4ab9de009e57f0cbe49b214"}}