Original paper
Data-driven deconvolution for large eddy simulations of Kraichnan turbulence
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
Journal
Volume
30
Issue
12
Pages
125109 - 125109
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Notes
History