Inverse design of graphene metamaterial based on machine learning and evolutionary algorithms

Abstract

We propose an intelligent approach to achieve inverse design by different regression algorithms for the double-layers graphene metamaterial (GM) structure. Compared with the ANNs, simple regression algorithms have advantage in accuracy and efficiency.