{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:ETMS57WG6SH6B5FEWTVG5FEXVW","short_pith_number":"pith:ETMS57WG","canonical_record":{"source":{"id":"2209.08052","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"physics.flu-dyn","submitted_at":"2022-09-16T17:08:10Z","cross_cats_sorted":["nlin.CD"],"title_canon_sha256":"dfc0fb7805d97e90b5be8730d28211f97f85c33aff3f665e3d7c624087581313","abstract_canon_sha256":"7d5b2d2aff24cde7ef4b6246f0f4ea1df5de7afbe3eaae176c81ae3418072d7e"},"schema_version":"1.0"},"canonical_sha256":"24d92efec6f48fe0f4a4b4ea6e9497ad93085f99cd8d0073a2e4946ba2ed82d1","source":{"kind":"arxiv","id":"2209.08052","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.08052","created_at":"2026-07-05T05:56:14Z"},{"alias_kind":"arxiv_version","alias_value":"2209.08052v2","created_at":"2026-07-05T05:56:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.08052","created_at":"2026-07-05T05:56:14Z"},{"alias_kind":"pith_short_12","alias_value":"ETMS57WG6SH6","created_at":"2026-07-05T05:56:14Z"},{"alias_kind":"pith_short_16","alias_value":"ETMS57WG6SH6B5FE","created_at":"2026-07-05T05:56:14Z"},{"alias_kind":"pith_short_8","alias_value":"ETMS57WG","created_at":"2026-07-05T05:56:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:ETMS57WG6SH6B5FEWTVG5FEXVW","target":"record","payload":{"canonical_record":{"source":{"id":"2209.08052","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"physics.flu-dyn","submitted_at":"2022-09-16T17:08:10Z","cross_cats_sorted":["nlin.CD"],"title_canon_sha256":"dfc0fb7805d97e90b5be8730d28211f97f85c33aff3f665e3d7c624087581313","abstract_canon_sha256":"7d5b2d2aff24cde7ef4b6246f0f4ea1df5de7afbe3eaae176c81ae3418072d7e"},"schema_version":"1.0"},"canonical_sha256":"24d92efec6f48fe0f4a4b4ea6e9497ad93085f99cd8d0073a2e4946ba2ed82d1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:56:14.632162Z","signature_b64":"tpe+2tQkpuxa31VgIxwHPMeXz0rHkTlJnD7xoVBH0GAX1WtL4PvRon5LGE3l+leRdQvGWRKsiHlggPOozDItDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"24d92efec6f48fe0f4a4b4ea6e9497ad93085f99cd8d0073a2e4946ba2ed82d1","last_reissued_at":"2026-07-05T05:56:14.631551Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:56:14.631551Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2209.08052","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":"2026-07-05T05:56:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"50m00+XHlnrUuvDptpw8hd0v0JuiyXYeIwZUNOKqTp8QKNciWFYbqR1LEG96AmnYiAdeTcpo6GETUKiKT5CIBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-20T17:31:32.372473Z"},"content_sha256":"51b86faeeec5e4d4947e49f2563cf7a33ea8f6923870770add1ef7497c966fe7","schema_version":"1.0","event_id":"sha256:51b86faeeec5e4d4947e49f2563cf7a33ea8f6923870770add1ef7497c966fe7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:ETMS57WG6SH6B5FEWTVG5FEXVW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Autoregressive Transformers for Data-Driven Spatio-Temporal Learning of Turbulent Flows","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["nlin.CD"],"primary_cat":"physics.flu-dyn","authors_text":"Aakash Patil, Elie Hachem, Jonathan Viquerat","submitted_at":"2022-09-16T17:08:10Z","abstract_excerpt":"A convolutional encoder-decoder-based transformer model is proposed for autoregressively training on spatio-temporal data of turbulent flows. The prediction of future fluid flow fields is based on the previously predicted fluid flow field to ensure long-term predictions without diverging. A combination of convolutional neural networks and transformer architecture is utilized to handle both the spatial and temporal dimensions of the data. To assess the performance of the model, a priori assessments are conducted, and significant agreements are found with the ground truth data. The a posteriori "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.08052","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/2209.08052/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":"2026-07-05T05:56:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WZnHrdpq3dhOAaCkfBK7PHtyRT9J0KgtLA6stB/RTyPBmrbLGQvYfwl+P8bah2zPaxuwNNL+F9rjuH6NNddYDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-20T17:31:32.372842Z"},"content_sha256":"5907d7705a2e14f298f68c434abf51e34384ae7351bc3fe239e91fe1086f1009","schema_version":"1.0","event_id":"sha256:5907d7705a2e14f298f68c434abf51e34384ae7351bc3fe239e91fe1086f1009"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ETMS57WG6SH6B5FEWTVG5FEXVW/bundle.json","state_url":"https://pith.science/pith/ETMS57WG6SH6B5FEWTVG5FEXVW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ETMS57WG6SH6B5FEWTVG5FEXVW/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":"2026-07-20T17:31:32Z","links":{"resolver":"https://pith.science/pith/ETMS57WG6SH6B5FEWTVG5FEXVW","bundle":"https://pith.science/pith/ETMS57WG6SH6B5FEWTVG5FEXVW/bundle.json","state":"https://pith.science/pith/ETMS57WG6SH6B5FEWTVG5FEXVW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ETMS57WG6SH6B5FEWTVG5FEXVW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:ETMS57WG6SH6B5FEWTVG5FEXVW","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":"7d5b2d2aff24cde7ef4b6246f0f4ea1df5de7afbe3eaae176c81ae3418072d7e","cross_cats_sorted":["nlin.CD"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"physics.flu-dyn","submitted_at":"2022-09-16T17:08:10Z","title_canon_sha256":"dfc0fb7805d97e90b5be8730d28211f97f85c33aff3f665e3d7c624087581313"},"schema_version":"1.0","source":{"id":"2209.08052","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.08052","created_at":"2026-07-05T05:56:14Z"},{"alias_kind":"arxiv_version","alias_value":"2209.08052v2","created_at":"2026-07-05T05:56:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.08052","created_at":"2026-07-05T05:56:14Z"},{"alias_kind":"pith_short_12","alias_value":"ETMS57WG6SH6","created_at":"2026-07-05T05:56:14Z"},{"alias_kind":"pith_short_16","alias_value":"ETMS57WG6SH6B5FE","created_at":"2026-07-05T05:56:14Z"},{"alias_kind":"pith_short_8","alias_value":"ETMS57WG","created_at":"2026-07-05T05:56:14Z"}],"graph_snapshots":[{"event_id":"sha256:5907d7705a2e14f298f68c434abf51e34384ae7351bc3fe239e91fe1086f1009","target":"graph","created_at":"2026-07-05T05:56:14Z","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/2209.08052/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"A convolutional encoder-decoder-based transformer model is proposed for autoregressively training on spatio-temporal data of turbulent flows. The prediction of future fluid flow fields is based on the previously predicted fluid flow field to ensure long-term predictions without diverging. A combination of convolutional neural networks and transformer architecture is utilized to handle both the spatial and temporal dimensions of the data. To assess the performance of the model, a priori assessments are conducted, and significant agreements are found with the ground truth data. The a posteriori ","authors_text":"Aakash Patil, Elie Hachem, Jonathan Viquerat","cross_cats":["nlin.CD"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"physics.flu-dyn","submitted_at":"2022-09-16T17:08:10Z","title":"Autoregressive Transformers for Data-Driven Spatio-Temporal Learning of Turbulent Flows"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.08052","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":"sha256:51b86faeeec5e4d4947e49f2563cf7a33ea8f6923870770add1ef7497c966fe7","target":"record","created_at":"2026-07-05T05:56:14Z","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":"7d5b2d2aff24cde7ef4b6246f0f4ea1df5de7afbe3eaae176c81ae3418072d7e","cross_cats_sorted":["nlin.CD"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"physics.flu-dyn","submitted_at":"2022-09-16T17:08:10Z","title_canon_sha256":"dfc0fb7805d97e90b5be8730d28211f97f85c33aff3f665e3d7c624087581313"},"schema_version":"1.0","source":{"id":"2209.08052","kind":"arxiv","version":2}},"canonical_sha256":"24d92efec6f48fe0f4a4b4ea6e9497ad93085f99cd8d0073a2e4946ba2ed82d1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"24d92efec6f48fe0f4a4b4ea6e9497ad93085f99cd8d0073a2e4946ba2ed82d1","first_computed_at":"2026-07-05T05:56:14.631551Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:56:14.631551Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tpe+2tQkpuxa31VgIxwHPMeXz0rHkTlJnD7xoVBH0GAX1WtL4PvRon5LGE3l+leRdQvGWRKsiHlggPOozDItDA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:56:14.632162Z","signed_message":"canonical_sha256_bytes"},"source_id":"2209.08052","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:51b86faeeec5e4d4947e49f2563cf7a33ea8f6923870770add1ef7497c966fe7","sha256:5907d7705a2e14f298f68c434abf51e34384ae7351bc3fe239e91fe1086f1009"],"state_sha256":"a7e72d9aaec639d6bb2eab7693cf5c5b1f8c7cade0d12a7cc122e81799c975aa"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ccWvg1dtygRzJpPyXJzWYba0wbc+1y3oxElgmEQgDQeMtvzmQkGOR3ciNXsnffWqyomm9VGjPy1YW6cMuYC3CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-20T17:31:32.378325Z","bundle_sha256":"28ac2828c74cfea9f5fa5e22ae03ab5edf64a36d8f0a0a1efb8ed08c553774d8"}}