{"paper":{"title":"Multi-scale Dynamic Wake Modeling and Prediction of Floating Offshore Wind Turbines via Physics-Informed Neural Networks and Fourier Neural Operators","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Fourier neural operators reconstruct multi-scale turbulent wakes of floating offshore wind turbines more accurately and faster than physics-informed neural networks.","cross_cats":["cs.LG"],"primary_cat":"physics.flu-dyn","authors_text":"Chang Xu, Guodan Dong, Jianhua Qin","submitted_at":"2026-04-27T01:21:05Z","abstract_excerpt":"Multi-scale dynamic wake modeling and prediction are essential for the real-time control and optimization of floating offshore wind turbines (FOWTs). In this study, wakes of FOWTs under coupled surge and pitch motions across a range of Strouhal numbers (St), which can induce wake meandering, are modeled via two novel deep-learning frameworks: physics-informed neural networks (PINNs) and Fourier neural operators (FNOs). The high-fidelity dataset is obtained from large-eddy simulations with the actuator line model (LES-AL). The results demonstrate that the dominant large-scale dynamic structures"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"FNO effectively resolves both large- and small-scale coherent turbulent structures with significantly higher fidelity... FNO achieves a training speed approximately eight times faster than PINN... PINN effectively acts as a spatiotemporal low-pass filter.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The CFD-generated training and test data accurately represent real-world FOWT wake physics across the full range of Strouhal numbers and motion amplitudes encountered in operation.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"FNO captures large- and small-scale wake structures, higher harmonics, and temporal variations more accurately and trains eight times faster than PINN for FOWT wake prediction.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Fourier neural operators reconstruct multi-scale turbulent wakes of floating offshore wind turbines more accurately and faster than physics-informed neural networks.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"29b58905f13a44a93e8f0c52cedd91944c3d56654e60e4fa7f1f2ac34a8f2261"},"source":{"id":"2604.23937","kind":"arxiv","version":2},"verdict":{"id":"2d49f22d-cfca-4c1c-a8c7-9d61d664a7b7","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-08T01:54:42.549635Z","strongest_claim":"FNO effectively resolves both large- and small-scale coherent turbulent structures with significantly higher fidelity... FNO achieves a training speed approximately eight times faster than PINN... PINN effectively acts as a spatiotemporal low-pass filter.","one_line_summary":"FNO captures large- and small-scale wake structures, higher harmonics, and temporal variations more accurately and trains eight times faster than PINN for FOWT wake prediction.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The CFD-generated training and test data accurately represent real-world FOWT wake physics across the full range of Strouhal numbers and motion amplitudes encountered in operation.","pith_extraction_headline":"Fourier neural operators reconstruct multi-scale turbulent wakes of floating offshore wind turbines more accurately and faster than physics-informed neural networks."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.23937/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_compliance","ran_at":"2026-05-19T22:33:54.199585Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"3ff40beb7cb254018f9264a9f99ec042f25e942e9065ffa3c610efce6c5db729"},"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"}