EMWP-RNN: A Physics-Encoded Recurrent Neural Network for Wave Propagation in Plasmas

Electromagnetic (EM) wave propagation and inversion in complex time-varying medium is a challenging problem, particularly for plasma applications. We extend the EM wave-plasma coupling physics computation mapping to the recurrent neural network (RNN). The system can be trained to learn inhomogeneous time-varying magnetized plasma parameters from temporal scattered field. As a proof-of-concept demonstration, a physics-encoded RNN has been verified, which encodes Maxwell's vector wave equation describing the multiphysics coupling system into the standard RNN architecture. The results demonstrate that time-varying plasma parameter inversion can be accomplished using only a few sets of transmitted electric fields. This model is interpretable and computationally efficient, benefiting from optimization strategies provided by deep learning, which may be extended for various EM-plasma interaction applications and beyond.