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• Perspective
• Published: 10 August 2023

The transformative potential of machine learning for experiments in fluid mechanics

• Ricardo Vinuesa   ORCID: orcid.org/0000-0001-6570-5499 1 , 2 ,
• Steven L. Brunton 3 &
• Beverley J. McKeon 4  

Nature Reviews Physics volume  5 ,  pages 536–545 ( 2023 ) Cite this article

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The field of machine learning (ML) has rapidly advanced the state of the art in many fields of science and engineering, including experimental fluid dynamics, which is one of the original big-data disciplines. This Perspective article highlights several aspects of experimental fluid mechanics that stand to benefit from progress in ML, including augmenting the fidelity and quality of measurement techniques, improving experimental design and surrogate digital-twin models and enabling real-time estimation and control. In each case, we discuss recent success stories and ongoing challenges, along with caveats and limitations, and outline the potential for new avenues of ML-augmented and ML-enabled experimental fluid mechanics.

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Acknowledgements

The authors gratefully acknowledge valuable discussions with B. Noack early in the development of this Perspective article. R.V. acknowledges financial support from ERC grant no. 2021-CoG-101043998, DEEPCONTROL. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. S.L.B. acknowledges support from the National Science Foundation AI Institute in Dynamic Systems (grant no. 2112085). B.J.M. is grateful for the support of the U.S. ONR through a Vannevar Bush Faculty Fellowship, N00014-17-1-3022.

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Vinuesa, R., Brunton, S.L. & McKeon, B.J. The transformative potential of machine learning for experiments in fluid mechanics. Nat Rev Phys 5 , 536–545 (2023). https://doi.org/10.1038/s42254-023-00622-y

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