Particle-based plasma simulation using a graph neural network
This work addresses the need for faster and differentiable simulators in plasma physics, offering potential for solving forward and inverse problems, though it is incremental as it applies an existing neural network approach to a specific domain.
The researchers tackled the challenge of accelerating particle-in-cell plasma simulations by developing a graph neural network surrogate model, which achieved high accuracy with a time step two orders of magnitude longer than conventional methods while reproducing fundamental instabilities like two-stream instabilities.
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.