{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:YG5VCGSQ7JX4LCUZXJ3VJR44Y6","short_pith_number":"pith:YG5VCGSQ","canonical_record":{"source":{"id":"1711.05501","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-11-15T11:30:20Z","cross_cats_sorted":["math.DS","physics.data-an"],"title_canon_sha256":"4e6f28cb77bc597174db8cb7b0cb64f001fa90a0720ac83771933a87574171ab","abstract_canon_sha256":"70fd5e38a9d9a20792d3b5328ca4e5b503c0e6e19be7dbac80b9b5e058abc304"},"schema_version":"1.0"},"canonical_sha256":"c1bb511a50fa6fc58a99ba7754c79cc7b59e2150641846c6825ace84251dc984","source":{"kind":"arxiv","id":"1711.05501","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.05501","created_at":"2026-05-17T23:52:08Z"},{"alias_kind":"arxiv_version","alias_value":"1711.05501v2","created_at":"2026-05-17T23:52:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.05501","created_at":"2026-05-17T23:52:08Z"},{"alias_kind":"pith_short_12","alias_value":"YG5VCGSQ7JX4","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_16","alias_value":"YG5VCGSQ7JX4LCUZ","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_8","alias_value":"YG5VCGSQ","created_at":"2026-05-18T12:31:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:YG5VCGSQ7JX4LCUZXJ3VJR44Y6","target":"record","payload":{"canonical_record":{"source":{"id":"1711.05501","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-11-15T11:30:20Z","cross_cats_sorted":["math.DS","physics.data-an"],"title_canon_sha256":"4e6f28cb77bc597174db8cb7b0cb64f001fa90a0720ac83771933a87574171ab","abstract_canon_sha256":"70fd5e38a9d9a20792d3b5328ca4e5b503c0e6e19be7dbac80b9b5e058abc304"},"schema_version":"1.0"},"canonical_sha256":"c1bb511a50fa6fc58a99ba7754c79cc7b59e2150641846c6825ace84251dc984","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:08.250994Z","signature_b64":"EWIfUoSMU7MzAjFo33L689ti3YzmjU0qjL5WdaORn2OlRE5RjTawCf00GvLwuE0KHtMJ8Z8ew9sDskGSYe4VDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c1bb511a50fa6fc58a99ba7754c79cc7b59e2150641846c6825ace84251dc984","last_reissued_at":"2026-05-17T23:52:08.250523Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:08.250523Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1711.05501","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":"2026-05-17T23:52:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vWxQeS4l7UhTCdpr5r1vdoUVVcdQTI0BiOBy+KIgqumugc5WgFPwQd/qUV136zYEzru/kRd3ne55jQycWrczBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T22:04:29.353062Z"},"content_sha256":"34ae703061ff17437617cb7285cb1fbe2d4bd4a6b37ccc04ea13c86c71d95ac5","schema_version":"1.0","event_id":"sha256:34ae703061ff17437617cb7285cb1fbe2d4bd4a6b37ccc04ea13c86c71d95ac5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:YG5VCGSQ7JX4LCUZXJ3VJR44Y6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Sparse identification of nonlinear dynamics for model predictive control in the low-data limit","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.DS","physics.data-an"],"primary_cat":"math.OC","authors_text":"Eurika Kaiser, J. Nathan Kutz, Steven L. Brunton","submitted_at":"2017-11-15T11:30:20Z","abstract_excerpt":"The data-driven discovery of dynamics via machine learning is currently pushing the frontiers of modeling and control efforts, and it provides a tremendous opportunity to extend the reach of model predictive control. However, many leading methods in machine learning, such as neural networks, require large volumes of training data, may not be interpretable, do not easily include known constraints and symmetries, and often do not generalize beyond the attractor where models are trained. These factors limit the use of these techniques for the online identification of a model in the low-data limit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.05501","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":""},"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":"2026-05-17T23:52:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cERb8nFawOykD3ZEHVXniA4UN0lWypMEhGeQVex319KrTZOYjbt0T4fXOyRr0d8zCI1kVXPeqGK19qt25mrUBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T22:04:29.353420Z"},"content_sha256":"a3fd4b8a3bb362725d54492841ab15e7972390a137a0a65a415776fba7c0a075","schema_version":"1.0","event_id":"sha256:a3fd4b8a3bb362725d54492841ab15e7972390a137a0a65a415776fba7c0a075"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YG5VCGSQ7JX4LCUZXJ3VJR44Y6/bundle.json","state_url":"https://pith.science/pith/YG5VCGSQ7JX4LCUZXJ3VJR44Y6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YG5VCGSQ7JX4LCUZXJ3VJR44Y6/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":"2026-05-20T22:04:29Z","links":{"resolver":"https://pith.science/pith/YG5VCGSQ7JX4LCUZXJ3VJR44Y6","bundle":"https://pith.science/pith/YG5VCGSQ7JX4LCUZXJ3VJR44Y6/bundle.json","state":"https://pith.science/pith/YG5VCGSQ7JX4LCUZXJ3VJR44Y6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YG5VCGSQ7JX4LCUZXJ3VJR44Y6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:YG5VCGSQ7JX4LCUZXJ3VJR44Y6","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":"70fd5e38a9d9a20792d3b5328ca4e5b503c0e6e19be7dbac80b9b5e058abc304","cross_cats_sorted":["math.DS","physics.data-an"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-11-15T11:30:20Z","title_canon_sha256":"4e6f28cb77bc597174db8cb7b0cb64f001fa90a0720ac83771933a87574171ab"},"schema_version":"1.0","source":{"id":"1711.05501","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.05501","created_at":"2026-05-17T23:52:08Z"},{"alias_kind":"arxiv_version","alias_value":"1711.05501v2","created_at":"2026-05-17T23:52:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.05501","created_at":"2026-05-17T23:52:08Z"},{"alias_kind":"pith_short_12","alias_value":"YG5VCGSQ7JX4","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_16","alias_value":"YG5VCGSQ7JX4LCUZ","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_8","alias_value":"YG5VCGSQ","created_at":"2026-05-18T12:31:56Z"}],"graph_snapshots":[{"event_id":"sha256:a3fd4b8a3bb362725d54492841ab15e7972390a137a0a65a415776fba7c0a075","target":"graph","created_at":"2026-05-17T23:52:08Z","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 data-driven discovery of dynamics via machine learning is currently pushing the frontiers of modeling and control efforts, and it provides a tremendous opportunity to extend the reach of model predictive control. However, many leading methods in machine learning, such as neural networks, require large volumes of training data, may not be interpretable, do not easily include known constraints and symmetries, and often do not generalize beyond the attractor where models are trained. These factors limit the use of these techniques for the online identification of a model in the low-data limit","authors_text":"Eurika Kaiser, J. Nathan Kutz, Steven L. Brunton","cross_cats":["math.DS","physics.data-an"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-11-15T11:30:20Z","title":"Sparse identification of nonlinear dynamics for model predictive control in the low-data limit"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.05501","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":"sha256:34ae703061ff17437617cb7285cb1fbe2d4bd4a6b37ccc04ea13c86c71d95ac5","target":"record","created_at":"2026-05-17T23:52:08Z","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":"70fd5e38a9d9a20792d3b5328ca4e5b503c0e6e19be7dbac80b9b5e058abc304","cross_cats_sorted":["math.DS","physics.data-an"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-11-15T11:30:20Z","title_canon_sha256":"4e6f28cb77bc597174db8cb7b0cb64f001fa90a0720ac83771933a87574171ab"},"schema_version":"1.0","source":{"id":"1711.05501","kind":"arxiv","version":2}},"canonical_sha256":"c1bb511a50fa6fc58a99ba7754c79cc7b59e2150641846c6825ace84251dc984","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c1bb511a50fa6fc58a99ba7754c79cc7b59e2150641846c6825ace84251dc984","first_computed_at":"2026-05-17T23:52:08.250523Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:52:08.250523Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EWIfUoSMU7MzAjFo33L689ti3YzmjU0qjL5WdaORn2OlRE5RjTawCf00GvLwuE0KHtMJ8Z8ew9sDskGSYe4VDQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:52:08.250994Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.05501","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:34ae703061ff17437617cb7285cb1fbe2d4bd4a6b37ccc04ea13c86c71d95ac5","sha256:a3fd4b8a3bb362725d54492841ab15e7972390a137a0a65a415776fba7c0a075"],"state_sha256":"5355865e12c1f4646561d74db4990d1811f22030edec059863adc95a11cd4d65"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zIY+o/8id5v/9U9YTSHmkIve44D5VvB56UBTVIunZwW+7u14KCrqy38vNiVEZd9zY8mnapbaDMY69s5iCEXMCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-20T22:04:29.355752Z","bundle_sha256":"69bf5f440cd653951a0e36d97ab3046160aa3e699d9236d188c7965c423324cb"}}