ArticleNature communications2026
Generative AI for efficient statistical computation of fluids.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Generative AI for efficient statistical computation of fluids.Nature communications · 2026Article
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Authors and funding
12 authors.
Funding
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Abstract
We present GenCFD, a generative modeling approach for fast, accurate, and robust statistical computation of three-dimensional turbulent fluid flows. While motivated from conditional score-based diffusion models, GenCFD is both empirically and theoretically validated on generating turbulent flows. Extensive numerical experimentation of challenging three-dimensional fluids demonstrates that GenCFD provides an accurate approximation of relevant statistical quantities of interest while also efficiently generating high-quality realistic samples of such flows. Moreover, we present rigorous theoretical results on analytically tractable models with mathematically relevant features of turbulent fluid flows. The analysis uncovers the mechanism underlying the success of the diffusion modeling approach. In particular, we highlight the importance of modeling distributions by GenCFD, while the mean-square-loss used by the deterministic machine learning approaches fails to accurately characterize statistical features of chaotic dynamics.
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Registered trials
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