Numerical Simulation of Streamer Discharge Using Physics-Informed Neural Networks
Changzhi Peng, Ruth V. Sabariego, Xuzhu Dong, Jiangjun Ruan
Abstract
The streamer discharge physical process can be described by the coupled Poisson’s equation and the convection–diffusion equation. It is a multi-physical field problem involving electromagnetism and hydrodynamics. We propose a streamer discharge model based on physics-informed neural networks (PINNs) to improve the computational efficiency regarding the classical approach. Poisson’s equation and the convection–diffusion equation are trained to generate sufficient data and construct a deep operator network (DeepONet). The performance of the PINN, in terms of accuracy, is analyzed by applying it to different datasets (electron density and potential distribution) and comparing to a reference solution (spatial evolution of electrons). The simulation results show that the neural network (NN) has high accuracy in learning two types of equations.