  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.
