{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:ZNE7KIKS5IHGZTRCLPR3H245DB","short_pith_number":"pith:ZNE7KIKS","schema_version":"1.0","canonical_sha256":"cb49f52152ea0e6cce225be3b3eb9d187965e34ea0edafc7bef9fbe5eecb77d1","source":{"kind":"arxiv","id":"1905.11075","version":3},"attestation_state":"computed","paper":{"title":"Machine Learning for Fluid Mechanics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"physics.flu-dyn","authors_text":"Bernd Noack, Petros Koumoutsakos, Steven Brunton","submitted_at":"2019-05-27T09:26:17Z","abstract_excerpt":"The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for"},"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":"1905.11075","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.flu-dyn","submitted_at":"2019-05-27T09:26:17Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"4bd48ab94c84f81af99385c28f92d298165e6b41e1a8096f8502f6a3b45efbc3","abstract_canon_sha256":"8590f8a71c246052ed687a571126c3cc8e8cc9ee151d6886c391cae1a1381a85"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:41:29.106964Z","signature_b64":"HEu4OZbO7xrV7S1/H0om3cXujC1WcKddX1X+OAPgABpCGQJj9eC9OsDpTEem5yLW9CoYyM32qheQyut7LySoCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cb49f52152ea0e6cce225be3b3eb9d187965e34ea0edafc7bef9fbe5eecb77d1","last_reissued_at":"2026-07-05T00:41:29.106476Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:41:29.106476Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Machine Learning for Fluid Mechanics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"physics.flu-dyn","authors_text":"Bernd Noack, Petros Koumoutsakos, Steven Brunton","submitted_at":"2019-05-27T09:26:17Z","abstract_excerpt":"The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.11075","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1905.11075/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":"1905.11075","created_at":"2026-07-05T00:41:29.106534+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.11075v3","created_at":"2026-07-05T00:41:29.106534+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.11075","created_at":"2026-07-05T00:41:29.106534+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZNE7KIKS5IHG","created_at":"2026-07-05T00:41:29.106534+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZNE7KIKS5IHGZTRC","created_at":"2026-07-05T00:41:29.106534+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZNE7KIKS","created_at":"2026-07-05T00:41:29.106534+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":11,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.07385","citing_title":"JAX-FVM: A differentiable, entropy-stable finite volume solver on unstructured meshes for compressible flows","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2605.26324","citing_title":"Semigroup Consistency as a Diagnostic for Learned Physics Simulators","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2605.30112","citing_title":"Striding Across Reynolds Numbers: Representation Geometry in Neural PDE Generalisation","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2606.13336","citing_title":"Data-Driven Equation Discovery for Nonlinear Liquid Film Flows","ref_index":108,"is_internal_anchor":false},{"citing_arxiv_id":"2605.19076","citing_title":"The impact of observation density on Bayesian inversion of latent dynamics in shock-dominated flows","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2507.21437","citing_title":"PVD-ONet: A Multi-scale Neural Operator Method for Singularly Perturbed Boundary Layer Problems","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18953","citing_title":"FlowForge: A Staged Local Rollout Engine for Flow-Field Prediction","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2605.00510","citing_title":"Scale-Aware Adversarial Analysis: A Diagnostic for Generative AI in Multiscale Complex Systems","ref_index":91,"is_internal_anchor":false},{"citing_arxiv_id":"2604.26621","citing_title":"Large-eddy simulation nets (LESnets) based on physics-informed neural operator for wall-bounded turbulence","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2604.25617","citing_title":"AI-Powered Surrogate Modelling for Multiscale Combustion: A Critical Review and Opportunities","ref_index":36,"is_internal_anchor":false},{"citing_arxiv_id":"2604.25824","citing_title":"Discovery of Sparse Invariant Subgrid-Scale Closures via Dissipation-Controlled Training for Large Eddy Simulation on Anisotropic Grids","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZNE7KIKS5IHGZTRCLPR3H245DB","json":"https://pith.science/pith/ZNE7KIKS5IHGZTRCLPR3H245DB.json","graph_json":"https://pith.science/api/pith-number/ZNE7KIKS5IHGZTRCLPR3H245DB/graph.json","events_json":"https://pith.science/api/pith-number/ZNE7KIKS5IHGZTRCLPR3H245DB/events.json","paper":"https://pith.science/paper/ZNE7KIKS"},"agent_actions":{"view_html":"https://pith.science/pith/ZNE7KIKS5IHGZTRCLPR3H245DB","download_json":"https://pith.science/pith/ZNE7KIKS5IHGZTRCLPR3H245DB.json","view_paper":"https://pith.science/paper/ZNE7KIKS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.11075&json=true","fetch_graph":"https://pith.science/api/pith-number/ZNE7KIKS5IHGZTRCLPR3H245DB/graph.json","fetch_events":"https://pith.science/api/pith-number/ZNE7KIKS5IHGZTRCLPR3H245DB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZNE7KIKS5IHGZTRCLPR3H245DB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZNE7KIKS5IHGZTRCLPR3H245DB/action/storage_attestation","attest_author":"https://pith.science/pith/ZNE7KIKS5IHGZTRCLPR3H245DB/action/author_attestation","sign_citation":"https://pith.science/pith/ZNE7KIKS5IHGZTRCLPR3H245DB/action/citation_signature","submit_replication":"https://pith.science/pith/ZNE7KIKS5IHGZTRCLPR3H245DB/action/replication_record"}},"created_at":"2026-07-05T00:41:29.106534+00:00","updated_at":"2026-07-05T00:41:29.106534+00:00"}