Artificial neural network subgrid models of 2-D compressible magnetohydrodynamic turbulence. (arXiv:1912.11073v2 [physics.comp-ph] UPDATED)
<a href="http://arxiv.org/find/physics/1/au:+Rosofsky_S/0/1/0/all/0/1">Shawn G. Rosofsky</a>, <a href="http://arxiv.org/find/physics/1/au:+Huerta_E/0/1/0/all/0/1">E. A. Huerta</a>
We explore the suitability of deep learning to capture the physics of
subgrid-scale ideal magnetohydrodynamics turbulence of 2-D simulations of the
magnetized Kelvin-Helmholtz instability. We produce simulations at different
resolutions to systematically quantify the performance of neural network models
to reproduce the physics of these complex simulations. We compare the
performance of our neural networks with gradient models, which are extensively
used in the extensively in the magnetohydrodynamic literature. Our findings
indicate that neural networks significantly outperform gradient models at
reproducing the effects of magnetohydrodynamics turbulence. To the best of our
knowledge, this is the first exploratory study on the use of deep learning to
learn and reproduce the physics of magnetohydrodynamics turbulence.
We explore the suitability of deep learning to capture the physics of
subgrid-scale ideal magnetohydrodynamics turbulence of 2-D simulations of the
magnetized Kelvin-Helmholtz instability. We produce simulations at different
resolutions to systematically quantify the performance of neural network models
to reproduce the physics of these complex simulations. We compare the
performance of our neural networks with gradient models, which are extensively
used in the extensively in the magnetohydrodynamic literature. Our findings
indicate that neural networks significantly outperform gradient models at
reproducing the effects of magnetohydrodynamics turbulence. To the best of our
knowledge, this is the first exploratory study on the use of deep learning to
learn and reproduce the physics of magnetohydrodynamics turbulence.
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