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We explore the possibility of fully replacing a plasma physics kineticsimulator with a graph neural network-based simulator. We focus on this classof surrogate models given the similarity between their message-passing updatemechanism and the traditional physics solver update, and the possibility ofenforcing known physical priors into the graph construction and update. We showthat our model learns the kinetic plasma dynamics of the one-dimensional plasmamodel, a predecessor of contemporary kinetic plasma simulation codes, andrecovers a wide range of well-known kinetic plasma processes, including plasmathermalization, electrostatic fluctuations about thermal equilibrium, and thedrag on a fast sheet and Landau damping. We compare the performance against theoriginal plasma model in terms of run-time, conservation laws, and temporalevolution of key physical quantities. The limitations of the model arepresented and possible directions for higher-dimensional surrogate models forkinetic plasmas are discussed.

Do Graph Neural Networks Dream of Landau Damping? Insights from Kinetic Simulations of a Plasma Sheet Model