Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Rotational and Reflectional Equivariant Convolutional Neural Network for
  data-limited applications: Multiphase Flow demonstration   This article deals with approximating steady-state particle-resolved fluid
flow around a fixed particle of interest under the influence of randomly
distributed stationary particles in a dispersed multiphase setup using
Convolutional Neural Network (CNN). The considered problem involves rotational
symmetry about the mean velocity (streamwise) direction. Thus, this work
enforces this symmetry using $\mathbf{\textbf{SE(3)-equivariant}}$, special
Euclidean group of dimension 3, CNN architecture, which is translation and
three-dimensional rotation equivariant. This study mainly explores the
generalization capabilities and benefits of SE(3)-equivariant network. Accurate
synthetic flow fields for Reynolds number and particle volume fraction
combinations spanning over a range of [86.22, 172.96] and [0.11, 0.45]
respectively are produced with careful application of symmetry-aware
data-driven approach.

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The categories of this paper are: solv-int
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
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The categories of this paper are: eess
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
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The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol