{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:Q2CIL5ZIQIAFHHQGYTHAIN3JHY","short_pith_number":"pith:Q2CIL5ZI","schema_version":"1.0","canonical_sha256":"868485f7288200539e06c4ce0437693e39adc8fd7eaf07d9cc382322fb4d578b","source":{"kind":"arxiv","id":"1706.03531","version":1},"attestation_state":"computed","paper":{"title":"Sparse reduced-order modeling : Sensor-based dynamics to full-state estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.flu-dyn","authors_text":"Bernd R. Noack, Jean-Christophe Loiseau, Steven L. Brunton","submitted_at":"2017-06-12T09:22:34Z","abstract_excerpt":"We propose a general dynamic reduced-order modeling framework for typical experimental data: time-resolved sensor data and optional non-time-resolved PIV snapshots. This framework contains four steps. First, the sensor signals are lifted to a dynamic feature space. Second, we identify a sparse human-interpretable nonlinear dynamical system for the feature state based on the sparse identification of nonlinear dynamics (SINDy). Third, if PIV snapshots are available, a local linear mapping from the feature state to velocity fields is shown to be orders of magnitudes more accurate than optimal mod"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1706.03531","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.flu-dyn","submitted_at":"2017-06-12T09:22:34Z","cross_cats_sorted":[],"title_canon_sha256":"4b51bf6c7411e772dde39bede09efcaafaed6f442d1c6d892eb0e1ff124901c1","abstract_canon_sha256":"dfbbb0376828876b42a1dc065bd82b8cfdc7dacf80e453568099c3f171ae6b30"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:16:32.073506Z","signature_b64":"bX8WxdWBqT7np+CK7jdhpMNeqCkWpvYOtDN3cC9PrLjIi/DC5wWGovEnR0MMdBhG8bjKZJ+IflBrPpczdhyVCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"868485f7288200539e06c4ce0437693e39adc8fd7eaf07d9cc382322fb4d578b","last_reissued_at":"2026-05-18T00:16:32.073005Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:16:32.073005Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sparse reduced-order modeling : Sensor-based dynamics to full-state estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.flu-dyn","authors_text":"Bernd R. Noack, Jean-Christophe Loiseau, Steven L. Brunton","submitted_at":"2017-06-12T09:22:34Z","abstract_excerpt":"We propose a general dynamic reduced-order modeling framework for typical experimental data: time-resolved sensor data and optional non-time-resolved PIV snapshots. This framework contains four steps. First, the sensor signals are lifted to a dynamic feature space. Second, we identify a sparse human-interpretable nonlinear dynamical system for the feature state based on the sparse identification of nonlinear dynamics (SINDy). Third, if PIV snapshots are available, a local linear mapping from the feature state to velocity fields is shown to be orders of magnitudes more accurate than optimal mod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.03531","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1706.03531","created_at":"2026-05-18T00:16:32.073062+00:00"},{"alias_kind":"arxiv_version","alias_value":"1706.03531v1","created_at":"2026-05-18T00:16:32.073062+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.03531","created_at":"2026-05-18T00:16:32.073062+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q2CIL5ZIQIAF","created_at":"2026-05-18T12:31:37.085036+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q2CIL5ZIQIAFHHQG","created_at":"2026-05-18T12:31:37.085036+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q2CIL5ZI","created_at":"2026-05-18T12:31:37.085036+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Q2CIL5ZIQIAFHHQGYTHAIN3JHY","json":"https://pith.science/pith/Q2CIL5ZIQIAFHHQGYTHAIN3JHY.json","graph_json":"https://pith.science/api/pith-number/Q2CIL5ZIQIAFHHQGYTHAIN3JHY/graph.json","events_json":"https://pith.science/api/pith-number/Q2CIL5ZIQIAFHHQGYTHAIN3JHY/events.json","paper":"https://pith.science/paper/Q2CIL5ZI"},"agent_actions":{"view_html":"https://pith.science/pith/Q2CIL5ZIQIAFHHQGYTHAIN3JHY","download_json":"https://pith.science/pith/Q2CIL5ZIQIAFHHQGYTHAIN3JHY.json","view_paper":"https://pith.science/paper/Q2CIL5ZI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1706.03531&json=true","fetch_graph":"https://pith.science/api/pith-number/Q2CIL5ZIQIAFHHQGYTHAIN3JHY/graph.json","fetch_events":"https://pith.science/api/pith-number/Q2CIL5ZIQIAFHHQGYTHAIN3JHY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q2CIL5ZIQIAFHHQGYTHAIN3JHY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q2CIL5ZIQIAFHHQGYTHAIN3JHY/action/storage_attestation","attest_author":"https://pith.science/pith/Q2CIL5ZIQIAFHHQGYTHAIN3JHY/action/author_attestation","sign_citation":"https://pith.science/pith/Q2CIL5ZIQIAFHHQGYTHAIN3JHY/action/citation_signature","submit_replication":"https://pith.science/pith/Q2CIL5ZIQIAFHHQGYTHAIN3JHY/action/replication_record"}},"created_at":"2026-05-18T00:16:32.073062+00:00","updated_at":"2026-05-18T00:16:32.073062+00:00"}