A Deep Learning-Based Particle-in-Cell Method for Plasma Simulations

TL;DR

This paper introduces a novel Deep Learning-based Particle-in-Cell method for plasma simulations, demonstrating its ability to accurately model plasma instabilities and stability, marking progress in integrating AI with computational physics.

Contribution

The paper presents a new DL-based PIC method using neural networks to compute electric fields, showing its effectiveness in plasma instability simulations and stability analysis.

Findings

Accurately reproduces the growth rate of two-stream instability.

Maintains stability against cold-beam instability.

Does not conserve total energy and momentum.

Abstract

We design and develop a new Particle-in-Cell (PIC) method for plasma simulations using Deep-Learning (DL) to calculate the electric field from the electron phase space. We train a Multilayer Perceptron (MLP) and a Convolutional Neural Network (CNN) to solve the two-stream instability test. We verify that the DL-based MLP PIC method produces the correct results using the two-stream instability: the DL-based PIC provides the expected growth rate of the two-stream instability. The DL-based PIC does not conserve the total energy and momentum. However, the DL-based PIC method is stable against the cold-beam instability, affecting traditional PIC methods. This work shows that integrating DL technologies into traditional computational methods is a viable approach for developing next-generation PIC algorithms.