CNN-Genetic Algorithm Hybrid Approach for Rapid Inverse Design of Ultra-Wideband Metasurfaces

In recent years, metasurfaces have demonstrated immense potential in electromagnetic wave manipulation and antenna design. Traditional metasurface design methods heavily rely on complex numerical calculations and optimization algorithms, resulting in high computational costs and lengthy design cycles. This paper presents a novel inverse design approach combining Convolutional Neural Networks (CNN) and Genetic Algorithm for metasurfaces. This method leverages CNN's advantages in feature extraction while utilizing genetic algorithm's global optimization capabilities to achieve rapid and precise design of metasurface units. Experimental results show that this approach can reduce design time from hours to minutes compared to traditional methods while maintaining high design accuracy.