{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:ZXEXDD7VZPLQOYIJGT7AW4OPWN","short_pith_number":"pith:ZXEXDD7V","canonical_record":{"source":{"id":"1911.08655","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2019-11-20T01:16:57Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"9cc8358d040268e18b2a9fc559352a643b43e118c5e5d16edaee7d972673ad6a","abstract_canon_sha256":"5506af8843e8c7faceb4335bb5a7b186124e61e90888946bb3daa941f5de3d89"},"schema_version":"1.0"},"canonical_sha256":"cdc9718ff5cbd707610934fe0b71cfb37312d3c8e84c6c77b586d0a3ddf85e6d","source":{"kind":"arxiv","id":"1911.08655","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1911.08655","created_at":"2026-07-05T01:09:56Z"},{"alias_kind":"arxiv_version","alias_value":"1911.08655v4","created_at":"2026-07-05T01:09:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.08655","created_at":"2026-07-05T01:09:56Z"},{"alias_kind":"pith_short_12","alias_value":"ZXEXDD7VZPLQ","created_at":"2026-07-05T01:09:56Z"},{"alias_kind":"pith_short_16","alias_value":"ZXEXDD7VZPLQOYIJ","created_at":"2026-07-05T01:09:56Z"},{"alias_kind":"pith_short_8","alias_value":"ZXEXDD7V","created_at":"2026-07-05T01:09:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:ZXEXDD7VZPLQOYIJGT7AW4OPWN","target":"record","payload":{"canonical_record":{"source":{"id":"1911.08655","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2019-11-20T01:16:57Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"9cc8358d040268e18b2a9fc559352a643b43e118c5e5d16edaee7d972673ad6a","abstract_canon_sha256":"5506af8843e8c7faceb4335bb5a7b186124e61e90888946bb3daa941f5de3d89"},"schema_version":"1.0"},"canonical_sha256":"cdc9718ff5cbd707610934fe0b71cfb37312d3c8e84c6c77b586d0a3ddf85e6d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:09:56.699485Z","signature_b64":"ndGJ1u4aAkS2LE+QChBUtIXDsa/aZnCkobSDQ8eGbXHbdr0LGLCGwed0aFT1CXXXfp6b/xoqafmsS3jweGopAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cdc9718ff5cbd707610934fe0b71cfb37312d3c8e84c6c77b586d0a3ddf85e6d","last_reissued_at":"2026-07-05T01:09:56.699101Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:09:56.699101Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1911.08655","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:09:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QnLOaGjsGcAU+BAg9230FkbRPjwONvIjfs1MeAmCQO9dvjsYUH6XnpsVr4A75vBLLUKcGdQTCXJZQDYSYpXmCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T10:45:44.186809Z"},"content_sha256":"7b8b778904160ea5af9b71e4ce00f734e39aedc94ba71a8d792b552a42fef895","schema_version":"1.0","event_id":"sha256:7b8b778904160ea5af9b71e4ce00f734e39aedc94ba71a8d792b552a42fef895"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:ZXEXDD7VZPLQOYIJGT7AW4OPWN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards Physics-informed Deep Learning for Turbulent Flow Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"physics.comp-ph","authors_text":"Adrian Albert, Karthik Kashinath, Mustafa Mustafa, Rose Yu, Rui Wang","submitted_at":"2019-11-20T01:16:57Z","abstract_excerpt":"While deep learning has shown tremendous success in a wide range of domains, it remains a grand challenge to incorporate physical principles in a systematic manner to the design, training, and inference of such models. In this paper, we aim to predict turbulent flow by learning its highly nonlinear dynamics from spatiotemporal velocity fields of large-scale fluid flow simulations of relevance to turbulence modeling and climate modeling. We adopt a hybrid approach by marrying two well-established turbulent flow simulation techniques with deep learning. Specifically, we introduce trainable spect"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.08655","kind":"arxiv","version":4},"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/1911.08655/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":"2026-07-05T01:09:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PlqStuL1VP0rCdwVGbicJWf/iBNSETxIjPkQzd4zjsfwQKgqn3vfxfPv7zqmGVeTKcd0OGGModH+IaToVbU+Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T10:45:44.187281Z"},"content_sha256":"404e4a4e7036a0f249942b8b719900357d541a05fdbc77c9f955bf87de2d573b","schema_version":"1.0","event_id":"sha256:404e4a4e7036a0f249942b8b719900357d541a05fdbc77c9f955bf87de2d573b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZXEXDD7VZPLQOYIJGT7AW4OPWN/bundle.json","state_url":"https://pith.science/pith/ZXEXDD7VZPLQOYIJGT7AW4OPWN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZXEXDD7VZPLQOYIJGT7AW4OPWN/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":"2026-08-06T10:45:44Z","links":{"resolver":"https://pith.science/pith/ZXEXDD7VZPLQOYIJGT7AW4OPWN","bundle":"https://pith.science/pith/ZXEXDD7VZPLQOYIJGT7AW4OPWN/bundle.json","state":"https://pith.science/pith/ZXEXDD7VZPLQOYIJGT7AW4OPWN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZXEXDD7VZPLQOYIJGT7AW4OPWN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:ZXEXDD7VZPLQOYIJGT7AW4OPWN","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":"5506af8843e8c7faceb4335bb5a7b186124e61e90888946bb3daa941f5de3d89","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2019-11-20T01:16:57Z","title_canon_sha256":"9cc8358d040268e18b2a9fc559352a643b43e118c5e5d16edaee7d972673ad6a"},"schema_version":"1.0","source":{"id":"1911.08655","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1911.08655","created_at":"2026-07-05T01:09:56Z"},{"alias_kind":"arxiv_version","alias_value":"1911.08655v4","created_at":"2026-07-05T01:09:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.08655","created_at":"2026-07-05T01:09:56Z"},{"alias_kind":"pith_short_12","alias_value":"ZXEXDD7VZPLQ","created_at":"2026-07-05T01:09:56Z"},{"alias_kind":"pith_short_16","alias_value":"ZXEXDD7VZPLQOYIJ","created_at":"2026-07-05T01:09:56Z"},{"alias_kind":"pith_short_8","alias_value":"ZXEXDD7V","created_at":"2026-07-05T01:09:56Z"}],"graph_snapshots":[{"event_id":"sha256:404e4a4e7036a0f249942b8b719900357d541a05fdbc77c9f955bf87de2d573b","target":"graph","created_at":"2026-07-05T01:09:56Z","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/1911.08655/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While deep learning has shown tremendous success in a wide range of domains, it remains a grand challenge to incorporate physical principles in a systematic manner to the design, training, and inference of such models. In this paper, we aim to predict turbulent flow by learning its highly nonlinear dynamics from spatiotemporal velocity fields of large-scale fluid flow simulations of relevance to turbulence modeling and climate modeling. We adopt a hybrid approach by marrying two well-established turbulent flow simulation techniques with deep learning. Specifically, we introduce trainable spect","authors_text":"Adrian Albert, Karthik Kashinath, Mustafa Mustafa, Rose Yu, Rui Wang","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2019-11-20T01:16:57Z","title":"Towards Physics-informed Deep Learning for Turbulent Flow Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.08655","kind":"arxiv","version":4},"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":"sha256:7b8b778904160ea5af9b71e4ce00f734e39aedc94ba71a8d792b552a42fef895","target":"record","created_at":"2026-07-05T01:09:56Z","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":"5506af8843e8c7faceb4335bb5a7b186124e61e90888946bb3daa941f5de3d89","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2019-11-20T01:16:57Z","title_canon_sha256":"9cc8358d040268e18b2a9fc559352a643b43e118c5e5d16edaee7d972673ad6a"},"schema_version":"1.0","source":{"id":"1911.08655","kind":"arxiv","version":4}},"canonical_sha256":"cdc9718ff5cbd707610934fe0b71cfb37312d3c8e84c6c77b586d0a3ddf85e6d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cdc9718ff5cbd707610934fe0b71cfb37312d3c8e84c6c77b586d0a3ddf85e6d","first_computed_at":"2026-07-05T01:09:56.699101Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:09:56.699101Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ndGJ1u4aAkS2LE+QChBUtIXDsa/aZnCkobSDQ8eGbXHbdr0LGLCGwed0aFT1CXXXfp6b/xoqafmsS3jweGopAw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:09:56.699485Z","signed_message":"canonical_sha256_bytes"},"source_id":"1911.08655","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7b8b778904160ea5af9b71e4ce00f734e39aedc94ba71a8d792b552a42fef895","sha256:404e4a4e7036a0f249942b8b719900357d541a05fdbc77c9f955bf87de2d573b"],"state_sha256":"c513cc486caae3913fd505ed4d78e9064e566f6adbd490af4778a16e43112e86"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0N2WsU5oHHDGCTNOVx9cAKguU03atlT4EB3edvyzhTsXYhbL1FUDMCzRIpOM5WsOuAHT76VAMPfRjvW+h5GUDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T10:45:44.190600Z","bundle_sha256":"5290bd73d57480784232d709c1203cdf61a47ee975feabc3bf812b8e60d882b9"}}