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arXiv AI By Hanlin Kong, Jianan Wei, Yuntian Chen, Yujia Wang, Wenguan Wang

High-fidelity Three-Dimensional Flow Field Reconstruction Driven by Turbulent Fluid Dynamics

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The paper presents a turbulence‑aware framework for reconstructing high‑fidelity three‑dimensional flow fields from coarse‑grained data. It combines a multiscale neural operator with a state‑space model to enforce temporal consistency, incorporates Navier‑Stokes residuals as soft constraints, and uses a mixture‑of‑experts mechanism for unsupervised adaptation to unknown coarse‑graining. Experiments on canonical turbulent systems and atmospheric reanalysis data show reduced reconstruction errors and better preservation of multiscale spatial structure and coherent temporal evolution compared to existing learning‑based and classical baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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