用图神经网络加速等离子体仿真，精度高且快100倍。
Particle-based plasma simulation using a graph neural network
- 构建图结构表示电磁场，实现基于粒子的等离子体模拟。
- 在宽参数范围内复现两流不稳定性，预测精度高，时间步长快100倍。
- 适合需要快速仿真和反问题求解的等离子体研究者使用。
提出一种基于图神经网络的粒子-网格等离子体模拟代理模型。通过构建图结构，在固定空间网格上表示电磁场。该模型在一维电子束场景下，覆盖广泛温度、漂移动量和密度范围，成功再现了两流不稳定性这一常见且基础的等离子体不稳定性。定性上观察到对向电子束的相空间混合特征；定量评估涵盖数密度分布、电场及其傅里叶分解（特别是最快增长模态的增长率）、粒子位置与动量分布、能量守恒及运行时间。模型在时间步长比传统方法长两个数量级的情况下仍保持高精度，证明复杂等离子体动力学可被学习，并展示了发展快速可微仿真器的潜力，适用于等离子体物理中的正向与反向问题求解。
原文摘要 · Abstract (English)
A surrogate model for particle-in-cell plasma simulations based on a graph neural network is presented. The graph is constructed in such a way as to enable the representation of electromagnetic fields on a fixed spatial grid. The model is applied to simulate beams of electrons in one dimension over a wide range of temperatures, drift momenta and densities, and is shown to reproduce two-stream instabilities - a common and fundamental plasma instability. Qualitatively, the characteristic phase-space mixing of counterpropagating electron beams is observed. Quantitatively, the model's performance is evaluated in terms of the accuracy of its predictions of number density distributions, the electric field, and their Fourier decompositions, particularly the growth rate of the fastest-growing unstable mode, as well as particle position, momentum distributions, energy conservation and run time. The model achieves high accuracy with a time step longer than conventional simulation by two orders of magnitude. This work demonstrates that complex plasma dynamics can be learned and shows promise for the development of fast differentiable simulators suitable for solving forward and inverse problems in plasma physics.
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