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.

通过图神经网络模拟器完全替代等离子体物理学动力学模拟器的可能性进行探索，着重于这一类替代模型，因为它们的消息传递更新机制与传统物理求解器更新机制相似，并且可以将已知的物理先验知识应用于图构建和更新，我们展示了模型学习了一维等离子体模型的运动动力学，这是当代等离子体动力学模拟代码的前身，并可以模拟等离子体热平衡、热平衡周围的静电扰动以及快速片上的阻力和朗道阻尼等多种已知的等离子体动力学过程，我们通过运行时间、守恒定律和关键物理量的时间演化来比较与原始等离子体模型的性能，同时讨论了对于高维等离子体替代模型的可能方向和模型的限制。