  In this paper, we study the linear transport model by adopting the deep
learning method, in particular the deep neural network (DNN) approach. While
the interest of using DNN to study partial differential equations is arising,
here we adapt it to study kinetic models, in particular the linear transport
model. Moreover, theoretical analysis on the convergence of the neural network
and its approximated solution towards the analytic solution is shown. We
demonstrate the accuracy and effectiveness of the proposed DNN method in the
numerical experiments.
