Low-temperature plasma simulation based on physics-informed neural networks: frameworks and preliminary applications

TL;DR

This paper introduces two physics-informed neural network frameworks, CS-PINN and RK-PINN, for low-temperature plasma simulation, demonstrating their effectiveness in modeling plasma behavior and transient phenomena with limited data.

Contribution

The paper presents novel AI-driven frameworks, CS-PINN and RK-PINN, specifically designed for low-temperature plasma simulation, incorporating solution-dependent coefficients and large-time-step prediction capabilities.

Findings

Both frameworks accurately solve plasma equations.

RK-PINN effectively simulates transient plasmas with large time steps.

Frameworks perform well even with noisy data.

Abstract

Plasma simulation is an important and sometimes only approach to investigating plasma behavior. In this work, we propose two general AI-driven frameworks for low-temperature plasma simulation: Coefficient-Subnet Physics-Informed Neural Network (CS-PINN) and Runge-Kutta Physics-Informed Neural Network (RK-PINN). The CS-PINN uses either a neural network or an interpolation function (e.g. spline function) as the subnet to approximate solution-dependent coefficients (e.g. electron-impact cross sections, thermodynamic properties, transport coefficients, et al.) in plasma equations. On the basis of this, the RK-PINN incorporates the implicit Runge-Kutta formalism in neural networks to achieve a large-time-step prediction of transient plasmas. Both CS-PINN and RK-PINN learn the complex non-linear relationship mapping from spatio-temporal space to equation's solution. Based on these two frameworks, we demonstrate preliminary applications by four cases covering plasma kinetic and fluid modeling. The results verify that both CS-PINN and RK-PINN have good performance in solving plasma equations. Moreover, the RK-PINN has ability of yielding a good solution for transient plasma simulation with not only large time step but also limited noisy sensing data.