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.

翻译：我们探索了用图神经网络模拟器完全取代等离子体物理动力学模拟器的可能性。鉴于这类替代模型的消息传递更新机制与传统物理求解器更新机制之间的相似性，以及将已知物理先验知识融入图构建和更新的可能性，我们将研究重点聚焦于此。研究表明，该模型能够学习一维等离子体模型（当代动力学等离子体模拟代码的前身）的动力学过程，并复现大量著名的动力学等离子体过程，包括等离子体热化、热平衡态静电涨落、快速片层阻力以及朗道阻尼。我们从运行时间、守恒定律以及关键物理量的时间演化三个方面，将该模型与原等离子体模型的性能进行了对比。文中还指出了该模型的局限性，并探讨了开发更高维动力学等离子体替代模型的可能方向。