Optimization of Reconfigurable Metasurface Design Based on Machine Learning

With the rapid development of electromagnetic metasurface technology, how to efficiently design reconfigurable metasurfaces to address complex electromagnetic environments has become a critical issue. Traditional design methods rely heavily on extensive simulations and parameter optimization, which are time-consuming and labor-intensive. This paper proposes a machine learning-based optimization approach for reconfigurable metasurface design, utilizing Support Vector Machines (SVM) to model the mapping relationship between electromagnetic properties and metasurface structures. Through inverse design, this method enables rapid optimization. The proposed approach is validated using a dataset of 10,000 simulation cases. The results demonstrate that this method can significantly streamline the design process and improve efficiency, offering a novel intelligent optimization solution for metasurface design.