  We explore the possibility of fully replacing a plasma physics kinetic
simulator with a graph neural network-based simulator. We focus on this class
of surrogate models given the similarity between their message-passing update
mechanism and the traditional physics solver update, and the possibility of
enforcing known physical priors into the graph construction and update. We show
that our model learns the kinetic plasma dynamics of the one-dimensional plasma
model, a predecessor of contemporary kinetic plasma simulation codes, and
recovers a wide range of well-known kinetic plasma processes, including plasma
thermalization, electrostatic fluctuations about thermal equilibrium, and the
drag on a fast sheet and Landau damping. We compare the performance against the
original plasma model in terms of run-time, conservation laws, and temporal
evolution of key physical quantities. The limitations of the model are
presented and possible directions for higher-dimensional surrogate models for
kinetic plasmas are discussed.
