Physics-informed neural network for predicting electric field distributions and permittivities of circular split-ring resonators

Physics informed neural networks (PINNs) solve supervised learning tasks by incorporating partial differential equations describing the governing physics. We use a PINN based on Maxwell’s equations in the frequency domain to predict the electrical permittivity parameters, and hence the electric fields, of circular split-ring resonator-based metamaterials thereby bypassing full-wave solutions based on finite-element methods. We demonstrate the use of a PINN for the inverse prediction of the electrical permittivity of a circular split ring resonator metamaterial given the spatial e-field distributions at the resonant frequency. Our results validate the PINN framework for the inverse retrieval of permittivities from field distributions.