  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.
