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Revision as of 16:51, 6 July 2022
Abstract
We develop a wavelet-based three-dimensional convolutional neural network (WCNN3d) for superresolution of coarse-grained data of homogeneous isotropic turbulence. The turbulent flow data are computed by high resolution direct numerical simulation (DNS), while the coarse-grained data are obtained by applying a Gaussian filter to the DNS data. The CNNs are trained with the DNS data and the coarse-grained data. We compare vorticityand velocity-based approaches and assess the proposed
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Published on 05/07/22
Submitted on 05/07/22
Volume 1700 Data Science, Machine Learning and Artificial Intelligence, 2022
DOI: 10.23967/wccm-apcom.2022.013
Licence: CC BY-NC-SA license
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