  In this paper, we developed a new PINN-based model to predict the potential
of point-charged particles surrounded by conductive walls. As a result of the
proposed physics-informed neural network model, the mean square error and R2
score are less than 7% and more than 90% for the corresponding example
simulation, respectively. Results have been compared with typical neural
networks and random forest as a standard machine learning algorithm. The R2
score of the random forest model was 70%, and a standard neural network could
not be trained well. Besides, computing time is significantly reduced compared
to the finite element solver.
