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替代等离子体物理动理学模拟器的可能性。鉴于图神经网络的讯息传递更新机制与传统物理求解器更新过程的相似性，以及将已知物理先验融入图构建与更新的可行性，我们聚焦于这类替代模型。研究表明，该模型能够学习一维等离子体模型（当代动理学等离子体模拟代码的前身）的动理学等离子体动力学，并复现一系列著名的动理学等离子体过程，包括等离子体热化、热平衡附近的静电涨落、快速等离子体片的阻力效应以及朗道阻尼。我们从运行时间、守恒律及关键物理量时间演化三个维度，将模型性能与原始等离子体模型进行对比。同时指出了当前模型的局限性，并探讨了面向更高维动理学等离子体替代模型的可能发展方向。