Generative Adversarial Networks to infer velocity components in rotating turbulent flows

• URL: http://arxiv.org/abs/2301.07541v2
• Date: Fri, 3 Nov 2023 19:31:33 GMT
• Title: Generative Adversarial Networks to infer velocity components in rotating turbulent flows
• Authors: Tianyi Li, Michele Buzzicotti, Luca Biferale and Fabio Bonaccorso
• Abstract summary: We show that CNN and GAN always outperform EPOD both concerning point-wise and statistical reconstructions. The analysis is performed using both standard validation tools based on $L$ spatial distance between the prediction and the ground truth.
• Score: 2.0873604996221946
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inference problems for two-dimensional snapshots of rotating turbulent flows are studied. We perform a systematic quantitative benchmark of point-wise and statistical reconstruction capabilities of the linear Extended Proper Orthogonal Decomposition (EPOD) method, a non-linear Convolutional Neural Network (CNN) and a Generative Adversarial Network (GAN). We attack the important task of inferring one velocity component out of the measurement of a second one, and two cases are studied: (I) both components lay in the plane orthogonal to the rotation axis and (II) one of the two is parallel to the rotation axis. We show that EPOD method works well only for the former case where both components are strongly correlated, while CNN and GAN always outperform EPOD both concerning point-wise and statistical reconstructions. For case (II), when the input and output data are weakly correlated, all methods fail to reconstruct faithfully the point-wise information. In this case, only GAN is able to reconstruct the field in a statistical sense. The analysis is performed using both standard validation tools based on $L_2$ spatial distance between the prediction and the ground truth and more sophisticated multi-scale analysis using wavelet decomposition. Statistical validation is based on standard Jensen-Shannon divergence between the probability density functions, spectral properties and multi-scale flatness.

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