Frequency Selective Surface Intelligent Topology Design Based on Machine Learning Technique

To efficiently and conveniently realize the topology design, this work proposes an intelligent topological design method based on machine learning for frequency selective surface (FSS) structures from a novel perspective. In the proposed method, the input is a set of desired $\vert \text{S}11\vert$ curves, and the output is the predicted FSS structure presented as an image to directly reveal the distribution of binary pixels in the modeling domain. A numerical example of single-layer double-layer FSS structures involving large numbers of binary variables is employed to validate the effectiveness of the proposed method.