Machine Learning for Turbulent Fluid Dynamics


One of the most important outstanding questions in fluid dynamics is the study of how and why uniform flow transitions to chaotic motion, or turbulence. Turbulent flow is a vital aspect in the design of airplanes, cars, and boats, but also in climate science and chemical engineering. Unsupervised machine learning will be used to find coherent structures in simulated flow in a pipe. Results will be used to develop a statistical theory of the transition to turbulence. For more details, see the recent article: Allawala, A., Tobias, S. M., & Marston, J. B. (2020). Dimensional reduction of direct statistical simulation. Journal of Fluid Mechanics, 898, 533–18.

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Programming experience with either Python, Julia, or Swift.


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