{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:GJ7UDMWGOBMFFYROTFCY62VE3W","short_pith_number":"pith:GJ7UDMWG","schema_version":"1.0","canonical_sha256":"327f41b2c6705852e22e99458f6aa4ddbd6d54c0a2629c8a13192e6c62469df6","source":{"kind":"arxiv","id":"2409.06548","version":1},"attestation_state":"computed","paper":{"title":"Three-dimensional generative adversarial networks for turbulent flow estimation from wall measurements","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.flu-dyn","authors_text":"Alejandro G\\\"uemes, Andrea Ianiro, Antonio Cu\\'ellar, \\'Oscar Flores, Ricardo Vinuesa, Stefano Discetti","submitted_at":"2024-09-10T14:23:15Z","abstract_excerpt":"Different types of neural networks have been used to solve the flow sensing problem in turbulent flows, namely to estimate velocity in wall-parallel planes from wall measurements. Generative adversarial networks (GANs) are among the most promising methodologies, due to their more accurate estimations and better perceptual quality. This work tackles this flow sensing problem in the vicinity of the wall, addressing for the first time the reconstruction of the entire three-dimensional (3-D) field with a single network, i.e. a 3-D GAN. With this methodology, a single training and prediction proces"},"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":"2409.06548","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.flu-dyn","submitted_at":"2024-09-10T14:23:15Z","cross_cats_sorted":[],"title_canon_sha256":"f410806bb27198982903fc0dacd71efd9c0d9aa832f7e51db584faf3256f10af","abstract_canon_sha256":"f0b318d806739bce8e53f828a63c4b58489d1c14d8f0aa318eba09297c88ca9a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:05:26.263194Z","signature_b64":"CknQg81fm7aOOEH0PTJt8sOPIodfwoVAydLhSaz5GlLiX0Fi1L5eQIZScXLWy7BGepFyalrj4zuu4ivloBZ9CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"327f41b2c6705852e22e99458f6aa4ddbd6d54c0a2629c8a13192e6c62469df6","last_reissued_at":"2026-07-05T09:05:26.262675Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:05:26.262675Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Three-dimensional generative adversarial networks for turbulent flow estimation from wall measurements","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.flu-dyn","authors_text":"Alejandro G\\\"uemes, Andrea Ianiro, Antonio Cu\\'ellar, \\'Oscar Flores, Ricardo Vinuesa, Stefano Discetti","submitted_at":"2024-09-10T14:23:15Z","abstract_excerpt":"Different types of neural networks have been used to solve the flow sensing problem in turbulent flows, namely to estimate velocity in wall-parallel planes from wall measurements. Generative adversarial networks (GANs) are among the most promising methodologies, due to their more accurate estimations and better perceptual quality. This work tackles this flow sensing problem in the vicinity of the wall, addressing for the first time the reconstruction of the entire three-dimensional (3-D) field with a single network, i.e. a 3-D GAN. With this methodology, a single training and prediction proces"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.06548","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/2409.06548/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":"2409.06548","created_at":"2026-07-05T09:05:26.262731+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.06548v1","created_at":"2026-07-05T09:05:26.262731+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.06548","created_at":"2026-07-05T09:05:26.262731+00:00"},{"alias_kind":"pith_short_12","alias_value":"GJ7UDMWGOBMF","created_at":"2026-07-05T09:05:26.262731+00:00"},{"alias_kind":"pith_short_16","alias_value":"GJ7UDMWGOBMFFYRO","created_at":"2026-07-05T09:05:26.262731+00:00"},{"alias_kind":"pith_short_8","alias_value":"GJ7UDMWG","created_at":"2026-07-05T09:05:26.262731+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/GJ7UDMWGOBMFFYROTFCY62VE3W","json":"https://pith.science/pith/GJ7UDMWGOBMFFYROTFCY62VE3W.json","graph_json":"https://pith.science/api/pith-number/GJ7UDMWGOBMFFYROTFCY62VE3W/graph.json","events_json":"https://pith.science/api/pith-number/GJ7UDMWGOBMFFYROTFCY62VE3W/events.json","paper":"https://pith.science/paper/GJ7UDMWG"},"agent_actions":{"view_html":"https://pith.science/pith/GJ7UDMWGOBMFFYROTFCY62VE3W","download_json":"https://pith.science/pith/GJ7UDMWGOBMFFYROTFCY62VE3W.json","view_paper":"https://pith.science/paper/GJ7UDMWG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.06548&json=true","fetch_graph":"https://pith.science/api/pith-number/GJ7UDMWGOBMFFYROTFCY62VE3W/graph.json","fetch_events":"https://pith.science/api/pith-number/GJ7UDMWGOBMFFYROTFCY62VE3W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GJ7UDMWGOBMFFYROTFCY62VE3W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GJ7UDMWGOBMFFYROTFCY62VE3W/action/storage_attestation","attest_author":"https://pith.science/pith/GJ7UDMWGOBMFFYROTFCY62VE3W/action/author_attestation","sign_citation":"https://pith.science/pith/GJ7UDMWGOBMFFYROTFCY62VE3W/action/citation_signature","submit_replication":"https://pith.science/pith/GJ7UDMWGOBMFFYROTFCY62VE3W/action/replication_record"}},"created_at":"2026-07-05T09:05:26.262731+00:00","updated_at":"2026-07-05T09:05:26.262731+00:00"}