  This paper presents a convolutional neural network (CNN)-based deep learning
model, inspired from UNet with series of encoder and decoder units with skip
connections, for the simulation of microwave-plasma interaction. The microwave
propagation characteristics in complex plasma medium pertaining to
transmission, absorption and reflection primarily depends on the ratio of
electromagnetic (EM) wave frequency and electron plasma frequency, and the
plasma density profile. The scattering of a plane EM wave with fixed frequency
(1 GHz) and amplitude incident on a plasma medium with different gaussian
density profiles (in the range of $1\times 10^{17}-1\times 10^{22}{m^{-3}}$)
have been considered. The training data associated with microwave-plasma
interaction has been generated using 2D-FDTD (Finite Difference Time Domain)
based simulations. The trained deep learning model is then used to reproduce
the scattered electric field values for the 1GHz incident microwave on
different plasma profiles with error margin of less than 2\%. We propose a
complete deep learning (DL) based pipeline to train, validate and evaluate the
model. We compare the results of the network, using various metrics like SSIM
index, average percent error and mean square error, with the physical data
obtained from well-established FDTD based EM solvers. To the best of our
knowledge, this is the first effort towards exploring a DL based approach for
the simulation of complex microwave plasma interaction. The deep learning
technique proposed in this work is significantly fast as compared to the
existing computational techniques, and can be used as a new, prospective and
alternative computational approach for investigating microwave-plasma
interaction in a real time scenario.
