Plasma simulation is an important and sometimes only approach to investigating plasma behavior. We have proposed 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). 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, 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.

# AI Plasma

## Publications

**Linlin Zhong**, Bingyu Wu, Yifan Wang. Accelerating physics-informed neural network based 1D arc simulation by meta learning.

*Journal of Physics D: Applied Physics*, 2023.

**Linlin Zhong**, Bingyu Wu, Yifan Wang. Low-temperature plasma simulation based on physics-informed neural networks: frameworks and preliminary applications.

*Physics of Fluids*, 2022.

**Linlin Zhong**, Qi Gu, Bingyu Wu. Deep learning for thermal plasma simulation: Solving 1-D arc model as an example.

*Computer Physics Communications*, 2020.