  This work investigates the behavior of impurities in edge plasma of tokamaks
using high-resolution numerical simulations based on Hasegawa--Wakatani
equations. Specifically, it focuses on the behavior of inertial particles,
which has not been extensively studied in the field of plasma physics. Our
simulations utilize one-way coupling of a large number of inertial point
particles, which model plasma impurities. We observe that with Stokes number
($St$) which characterizes the inertia of particles being much less than one,
such light impurities closely track the fluid flow without pronounced
clustering. For intermediate $St$ values, distinct clustering appears, with
larger Stokes values, {\it i.e.} heavy impurities even generating more
substantial clusters. When $St$ is significantly large, very heavy impurities
tend to detach from the flow and maintain their trajectory, resulting in fewer
observable clusters and corresponding to random motion. A core component of
this work involves machine learning techniques. Applying three different neural
networks - Autoencoder, U-Net, and Generative Adversarial Network (GAN) - to
synthesize preferential concentration fields of impurities, we use vorticity as
input and predict impurity number density fields. GAN outperforms the two
others by aligning closely with direct numerical simulation data in terms of
probability density functions of the particle distribution and energy spectra.
This machine learning technique holds the potential to reduce computational
costs by eliminating the need to track millions of particles modeling
impurities in simulations.
