Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Artificial neural network subgrid models of 2-D compressible
  magnetohydrodynamic 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.

20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
1
The categories of this paper are: cs
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin