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Fluid dynamics

A new perspective on the simulation of stochastic problems in fluid mechanics with diffusion models

Nature Machine Intelligence volume 7, pages 816–817 (2025)Cite this article

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Generative deep learning models offer a fundamentally new approach for simulating stochastic processes in turbulent flows.

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Fig. 1: Flow of hot air rising from a candle.

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References

  1. Li, T., Biferale, L., Bonaccorso, F., Scarpolini, M. A. & Buzzicotti, M. Nat. Mach. Intell. 6, 393–403 (2024).

    Article  Google Scholar 

  2. Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N. & Ganguli, S. In Proc. 32nd International Conference on Machine Learning Vol. 37, 2256–2265 (PMLR, 2015).

  3. Toschi, F. & Bodenschatz, E. Annu. Rev. Fluid Mech. 41, 375–404 (2009).

    Article  Google Scholar 

  4. Vinuesa, R. & Brunton, S. L. Nat. Comput. Sci. 2, 358–366 (2022).

    Article  Google Scholar 

  5. Tennie, F., Laizet, S., Lloyd, S. & Magri, L. Nat. Rev. Physics 7, 220–230 (2025).

    Article  Google Scholar 

  6. Gourianov, N., Givi, P., Jaksch, D. & Pope, S. B. Sci. Adv. 11, 5990 (2025).

    Article  Google Scholar 

  7. Du, P., Parikh, M. H., Fan, X., Liu, X.-Y. & Wang, J.-X. Nat. Commun. 15, 10416 (2024).

    Article  Google Scholar 

  8. Valencia, M. L., Pfaff, T. & Thuerey, N. Learning distributions of complex fluid simulations with diffusion graph networks. In 13th International Conference on Learning Representations (OpenReview, 2025).

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Authors and Affiliations

  1. School of Computation, Information and Technology, Technical University Munich, Garching, Germany

    Luca Guastoni

  2. FLOW, Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden

    Ricardo Vinuesa

Authors
  1. Luca Guastoni
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  2. Ricardo Vinuesa
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Correspondence to Ricardo Vinuesa.

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The authors declare no competing interests.

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Guastoni, L., Vinuesa, R. A new perspective on the simulation of stochastic problems in fluid mechanics with diffusion models. Nat Mach Intell 7, 816–817 (2025). https://doi.org/10.1038/s42256-025-01060-4

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