Inferring Turbulent Parameters via Machine Learning. (arXiv:2201.00732v1 [physics.flu-dyn] CROSS LISTED)

We design a machine learning technique to solve the general problem of
inferring physical parameters from the observation of turbulent flows, a
relevant exercise in many theoretical and applied fields, from engineering to
earth observation and astrophysics. Our approach is to train the machine
learning system to regress the rotation frequency of the flow's reference
frame, from the observation of the flow's velocity amplitude on a 2d plane
extracted from the 3d domain. The machine learning approach consists of a …

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