Physics-informed neural network for inversely predicting effective electric permittivities of metamaterials

We apply a physics-informed neural network framework for inversely retrieving the effective material parameters of a two-dimensional metasurface from its scattered field(s). We show that by employing a loss function based on the Helmholtz wave equation, we can model the performance of a metamaterial disc-shaped structure and split-ring resonator with great promise and demonstrate the dependance of resonant behavior on the homogenized electric permittivity distribution profile generated by our network.