Data-driven deconvolution for large eddy simulations of Kraichnan turbulence

Volume: 30, Issue: 12, Pages: 125109 - 125109
Published: Dec 1, 2018
Abstract
In this article, we demonstrate the use of artificial neural networks as optimal maps which are utilized for convolution and deconvolution of coarse-grained fields to account for sub-grid scale turbulence effects. We demonstrate that an effective eddy-viscosity is predicted by our purely data-driven large eddy simulation framework without explicit utilization of phenomenological arguments. In addition, our data-driven framework precludes the...
Paper Details
Title
Data-driven deconvolution for large eddy simulations of Kraichnan turbulence
Published Date
Dec 1, 2018
Volume
30
Issue
12
Pages
125109 - 125109
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