Inferring Turbulent Parameters via Machine Learning
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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 Deep Convolutional Neural Network (DCNN) of the same kind developed in computer vision. The training and validation datasets are produced by means of fully resolved direct numerical simulations. This study shows interesting results from two different points of view. From the machine learning point of view it shows the potential of DCNN, reaching good results on such a particularly complex problem that goes well outside the limits of human vision. Second, from the physics point of view, it provides an example on how machine learning can be exploited in data analysis to infer information that would be inaccessible otherwise. Indeed, by comparing DCNN with the other possible Bayesian approaches, we find that DCNN yields to a much higher inference accuracy in all the examined cases.
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