Machine learning accelerated particle-in-cell plasma simulations

TL;DR

This paper introduces a neural network-augmented solver for particle-in-cell plasma simulations that reduces computational effort while maintaining conservation laws, enabling faster high-fidelity plasma modeling.

Contribution

It presents a novel integration of neural networks with implicit PIC methods to accelerate simulations without sacrificing accuracy or conservation properties.

Findings

Reduced solver iterations by approximately 25% in electron plasma oscillation simulations.

Neural network predicts solution subspace using fluid moments and electric field.

Potential to accelerate multi-scale plasma simulations while preserving conservation laws.

Abstract

Particle-In-Cell (PIC) methods are frequently used for kinetic, high-fidelity simulations of plasmas. Implicit formulations of PIC algorithms feature strong conservation properties, up to numerical round-off errors, and are not subject to time-step limitations which make them an attractive candidate to use in simulations fusion plasmas. Currently they remain prohibitively expensive for high-fidelity simulation of macroscopic plasmas. We investigate how amortized solvers can be incorporated with PIC methods for simulations of plasmas. Incorporated into the amortized solver, a neural network predicts a vector space that entails an approximate solution of the PIC system. The network uses only fluid moments and the electric field as input and its output is used to augment the vector space of an iterative linear solver. We find that this approach reduces the average number of required solver iterations by about 25% when simulating electron plasma oscillations. This novel approach may allow to accelerate implicit PIC simulations while retaining all conservation laws and may also be appropriate for multi-scale systems.