{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:WAX6RLWNWE3OOR2JPRFXWE2ISU","short_pith_number":"pith:WAX6RLWN","schema_version":"1.0","canonical_sha256":"b02fe8aecdb136e747497c4b7b13489524326386098e524f4ef5fab7e5e07683","source":{"kind":"arxiv","id":"2606.29047","version":1},"attestation_state":"computed","paper":{"title":"Weak Dominant Balance for Robust Identification of Dynamically Consistent Fluid Flow Structure","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","physics.flu-dyn"],"primary_cat":"cs.CE","authors_text":"Christian Lagemann, Esther Lagemann, H. Jane Bae, Kunihiko Taira, Ricardo Vinuesa, Samuel Ahnert, Steven L. Brunton","submitted_at":"2026-06-27T19:05:50Z","abstract_excerpt":"Extracting interpretable, localized physical mechanisms from complex spatiotemporal data is a foundational challenge across physics, biology, and engineering, but has remained out of reach on real measurements. The central obstacle is obtaining high-quality gradients of data via numerical differentiation, which amplifies noise, diverges for high-order equations, and falters on irregular geometries, limiting the scope of existing approaches to clean simulations of low-order systems. Here, we present weak dominant balance, a derivative-free framework that projects governing equations into a weak"},"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":"2606.29047","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CE","submitted_at":"2026-06-27T19:05:50Z","cross_cats_sorted":["cs.LG","physics.flu-dyn"],"title_canon_sha256":"ed64dfc663e0ea81063b5b3acc704f5012094f7498f63abacf30df2af8f59c4c","abstract_canon_sha256":"c47e8791214ed101c601dbaf44d7c5e9b34383d79c5786776346e3ba60a1a76c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T01:17:50.680673Z","signature_b64":"H/2P6cFscRLUMy2jsk+9c/slF9Xc3FNtuocDqqwS9llSPAyxk9EHoTSvzBBkZ3srXYaqGED3RfNyWsVSXKzlDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b02fe8aecdb136e747497c4b7b13489524326386098e524f4ef5fab7e5e07683","last_reissued_at":"2026-06-30T01:17:50.680188Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T01:17:50.680188Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Weak Dominant Balance for Robust Identification of Dynamically Consistent Fluid Flow Structure","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","physics.flu-dyn"],"primary_cat":"cs.CE","authors_text":"Christian Lagemann, Esther Lagemann, H. Jane Bae, Kunihiko Taira, Ricardo Vinuesa, Samuel Ahnert, Steven L. Brunton","submitted_at":"2026-06-27T19:05:50Z","abstract_excerpt":"Extracting interpretable, localized physical mechanisms from complex spatiotemporal data is a foundational challenge across physics, biology, and engineering, but has remained out of reach on real measurements. The central obstacle is obtaining high-quality gradients of data via numerical differentiation, which amplifies noise, diverges for high-order equations, and falters on irregular geometries, limiting the scope of existing approaches to clean simulations of low-order systems. Here, we present weak dominant balance, a derivative-free framework that projects governing equations into a weak"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.29047","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.29047/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.29047","created_at":"2026-06-30T01:17:50.680260+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.29047v1","created_at":"2026-06-30T01:17:50.680260+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.29047","created_at":"2026-06-30T01:17:50.680260+00:00"},{"alias_kind":"pith_short_12","alias_value":"WAX6RLWNWE3O","created_at":"2026-06-30T01:17:50.680260+00:00"},{"alias_kind":"pith_short_16","alias_value":"WAX6RLWNWE3OOR2J","created_at":"2026-06-30T01:17:50.680260+00:00"},{"alias_kind":"pith_short_8","alias_value":"WAX6RLWN","created_at":"2026-06-30T01:17:50.680260+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/WAX6RLWNWE3OOR2JPRFXWE2ISU","json":"https://pith.science/pith/WAX6RLWNWE3OOR2JPRFXWE2ISU.json","graph_json":"https://pith.science/api/pith-number/WAX6RLWNWE3OOR2JPRFXWE2ISU/graph.json","events_json":"https://pith.science/api/pith-number/WAX6RLWNWE3OOR2JPRFXWE2ISU/events.json","paper":"https://pith.science/paper/WAX6RLWN"},"agent_actions":{"view_html":"https://pith.science/pith/WAX6RLWNWE3OOR2JPRFXWE2ISU","download_json":"https://pith.science/pith/WAX6RLWNWE3OOR2JPRFXWE2ISU.json","view_paper":"https://pith.science/paper/WAX6RLWN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.29047&json=true","fetch_graph":"https://pith.science/api/pith-number/WAX6RLWNWE3OOR2JPRFXWE2ISU/graph.json","fetch_events":"https://pith.science/api/pith-number/WAX6RLWNWE3OOR2JPRFXWE2ISU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WAX6RLWNWE3OOR2JPRFXWE2ISU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WAX6RLWNWE3OOR2JPRFXWE2ISU/action/storage_attestation","attest_author":"https://pith.science/pith/WAX6RLWNWE3OOR2JPRFXWE2ISU/action/author_attestation","sign_citation":"https://pith.science/pith/WAX6RLWNWE3OOR2JPRFXWE2ISU/action/citation_signature","submit_replication":"https://pith.science/pith/WAX6RLWNWE3OOR2JPRFXWE2ISU/action/replication_record"}},"created_at":"2026-06-30T01:17:50.680260+00:00","updated_at":"2026-06-30T01:17:50.680260+00:00"}