Inferring Turbulent Parameters via Machine Learning
- URL: http://arxiv.org/abs/2201.00732v1
- Date: Mon, 3 Jan 2022 16:08:48 GMT
- Title: Inferring Turbulent Parameters via Machine Learning
- Authors: Michele Buzzicotti, Fabio Bonaccorso and Luca Biferale
- Abstract summary: We design a machine learning technique to solve the general problem of inferring physical parameters from the observation of turbulent flows.
Our approach is to train the machine learning system to regress the rotation frequency of the flow's reference frame.
This study shows interesting results from two different points of view.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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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