{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:GK7ORVYI44FVQ2FICLRN7KVT22","short_pith_number":"pith:GK7ORVYI","canonical_record":{"source":{"id":"1805.09917","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2018-05-23T16:55:16Z","cross_cats_sorted":["nlin.CD","physics.data-an","physics.flu-dyn","stat.ML"],"title_canon_sha256":"1ab7f71296d4efefbef0b83c416df7633e2a0d5530efc5b1cb6b7c930ead9d56","abstract_canon_sha256":"7d81443ef024cf3a523548f5a7a671c5e944cf459879f5a5afd26ec6f4166df8"},"schema_version":"1.0"},"canonical_sha256":"32bee8d708e70b5868a812e2dfaab3d68b30a58a1bc8d18e90280dbe5747ef1f","source":{"kind":"arxiv","id":"1805.09917","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.09917","created_at":"2026-05-18T00:06:02Z"},{"alias_kind":"arxiv_version","alias_value":"1805.09917v3","created_at":"2026-05-18T00:06:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.09917","created_at":"2026-05-18T00:06:02Z"},{"alias_kind":"pith_short_12","alias_value":"GK7ORVYI44FV","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"GK7ORVYI44FVQ2FI","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"GK7ORVYI","created_at":"2026-05-18T12:32:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:GK7ORVYI44FVQ2FICLRN7KVT22","target":"record","payload":{"canonical_record":{"source":{"id":"1805.09917","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2018-05-23T16:55:16Z","cross_cats_sorted":["nlin.CD","physics.data-an","physics.flu-dyn","stat.ML"],"title_canon_sha256":"1ab7f71296d4efefbef0b83c416df7633e2a0d5530efc5b1cb6b7c930ead9d56","abstract_canon_sha256":"7d81443ef024cf3a523548f5a7a671c5e944cf459879f5a5afd26ec6f4166df8"},"schema_version":"1.0"},"canonical_sha256":"32bee8d708e70b5868a812e2dfaab3d68b30a58a1bc8d18e90280dbe5747ef1f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:02.032494Z","signature_b64":"GHGedsq/bIik8qaXnsVNinWpOP8cQsv/4RmUX/Dc2GNy/lMZrdrUExmDYM2ZecdZvh86FyyPSoD1Ph1nCsKpAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"32bee8d708e70b5868a812e2dfaab3d68b30a58a1bc8d18e90280dbe5747ef1f","last_reissued_at":"2026-05-18T00:06:02.032090Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:02.032090Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.09917","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:06:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fIos5f7H1obP9eg663RsY5ItlnPw8ufTzCU1JRWc5mcLea0XR9ud5r7cErKT9FCvUaGHlKdKsw0ykHmdcxd7CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-25T09:21:59.799388Z"},"content_sha256":"b25aaaa933421ef5f88992e90ab8828c408cbc5ffcaac9c902b52b1e44404036","schema_version":"1.0","event_id":"sha256:b25aaaa933421ef5f88992e90ab8828c408cbc5ffcaac9c902b52b1e44404036"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:GK7ORVYI44FVQ2FICLRN7KVT22","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Machine-learning inference of fluid variables from data using reservoir computing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["nlin.CD","physics.data-an","physics.flu-dyn","stat.ML"],"primary_cat":"physics.comp-ph","authors_text":"Kengo Nakai, Yoshitaka Saiki","submitted_at":"2018-05-23T16:55:16Z","abstract_excerpt":"We infer both microscopic and macroscopic behaviors of a three-dimensional chaotic fluid flow using reservoir computing. In our procedure of the inference, we assume no prior knowledge of a physical process of a fluid flow except that its behavior is complex but deterministic. We present two ways of inference of the complex behavior; the first called partial-inference requires continued knowledge of partial time-series data during the inference as well as past time-series data, while the second called full-inference requires only past time-series data as training data. For the first case, we a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.09917","kind":"arxiv","version":3},"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":"2026-05-18T00:06:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lvQWZ0CcYYufFSGiJeewW8smqoAswTaEY5pJ9hUJ5AIQY+aUoZS0ubinusSpkhTdUaSLuoYvPiTkshGcVuK7Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-25T09:21:59.799735Z"},"content_sha256":"724a0d40352c93bfc41bfb6618d0d64ff828d50930d35fc1fc68a4708dca9036","schema_version":"1.0","event_id":"sha256:724a0d40352c93bfc41bfb6618d0d64ff828d50930d35fc1fc68a4708dca9036"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GK7ORVYI44FVQ2FICLRN7KVT22/bundle.json","state_url":"https://pith.science/pith/GK7ORVYI44FVQ2FICLRN7KVT22/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GK7ORVYI44FVQ2FICLRN7KVT22/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":"2026-06-25T09:21:59Z","links":{"resolver":"https://pith.science/pith/GK7ORVYI44FVQ2FICLRN7KVT22","bundle":"https://pith.science/pith/GK7ORVYI44FVQ2FICLRN7KVT22/bundle.json","state":"https://pith.science/pith/GK7ORVYI44FVQ2FICLRN7KVT22/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GK7ORVYI44FVQ2FICLRN7KVT22/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:GK7ORVYI44FVQ2FICLRN7KVT22","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":"7d81443ef024cf3a523548f5a7a671c5e944cf459879f5a5afd26ec6f4166df8","cross_cats_sorted":["nlin.CD","physics.data-an","physics.flu-dyn","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2018-05-23T16:55:16Z","title_canon_sha256":"1ab7f71296d4efefbef0b83c416df7633e2a0d5530efc5b1cb6b7c930ead9d56"},"schema_version":"1.0","source":{"id":"1805.09917","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.09917","created_at":"2026-05-18T00:06:02Z"},{"alias_kind":"arxiv_version","alias_value":"1805.09917v3","created_at":"2026-05-18T00:06:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.09917","created_at":"2026-05-18T00:06:02Z"},{"alias_kind":"pith_short_12","alias_value":"GK7ORVYI44FV","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"GK7ORVYI44FVQ2FI","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"GK7ORVYI","created_at":"2026-05-18T12:32:25Z"}],"graph_snapshots":[{"event_id":"sha256:724a0d40352c93bfc41bfb6618d0d64ff828d50930d35fc1fc68a4708dca9036","target":"graph","created_at":"2026-05-18T00:06:02Z","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":"We infer both microscopic and macroscopic behaviors of a three-dimensional chaotic fluid flow using reservoir computing. In our procedure of the inference, we assume no prior knowledge of a physical process of a fluid flow except that its behavior is complex but deterministic. We present two ways of inference of the complex behavior; the first called partial-inference requires continued knowledge of partial time-series data during the inference as well as past time-series data, while the second called full-inference requires only past time-series data as training data. For the first case, we a","authors_text":"Kengo Nakai, Yoshitaka Saiki","cross_cats":["nlin.CD","physics.data-an","physics.flu-dyn","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2018-05-23T16:55:16Z","title":"Machine-learning inference of fluid variables from data using reservoir computing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.09917","kind":"arxiv","version":3},"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:b25aaaa933421ef5f88992e90ab8828c408cbc5ffcaac9c902b52b1e44404036","target":"record","created_at":"2026-05-18T00:06:02Z","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":"7d81443ef024cf3a523548f5a7a671c5e944cf459879f5a5afd26ec6f4166df8","cross_cats_sorted":["nlin.CD","physics.data-an","physics.flu-dyn","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2018-05-23T16:55:16Z","title_canon_sha256":"1ab7f71296d4efefbef0b83c416df7633e2a0d5530efc5b1cb6b7c930ead9d56"},"schema_version":"1.0","source":{"id":"1805.09917","kind":"arxiv","version":3}},"canonical_sha256":"32bee8d708e70b5868a812e2dfaab3d68b30a58a1bc8d18e90280dbe5747ef1f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"32bee8d708e70b5868a812e2dfaab3d68b30a58a1bc8d18e90280dbe5747ef1f","first_computed_at":"2026-05-18T00:06:02.032090Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:06:02.032090Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GHGedsq/bIik8qaXnsVNinWpOP8cQsv/4RmUX/Dc2GNy/lMZrdrUExmDYM2ZecdZvh86FyyPSoD1Ph1nCsKpAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:06:02.032494Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.09917","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b25aaaa933421ef5f88992e90ab8828c408cbc5ffcaac9c902b52b1e44404036","sha256:724a0d40352c93bfc41bfb6618d0d64ff828d50930d35fc1fc68a4708dca9036"],"state_sha256":"e31ea33febc338dfdfd891c2ae836a3c67ab1a0f608565c272a25e89a6cf4401"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DCr6bW2bVXzhVMiC9ECCb5nAKuRq05EO2HudrP70Cq6Wi7/kDRA1wSw9nfIb/stGi4g7CRgr4AYW/kgks6KWAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-25T09:21:59.801564Z","bundle_sha256":"95c553a0bacedaf882b84bf83d4974b0a8b776e1d1f47232e9dccdd174e943d7"}}