Machine Learning Algorithms for Enhancement of a Dual Square FSS for Sub 6G Applications

Frequency Selective Surfaces (FSS) play a very critical role now-a-days in electromagnetic engineering, which enables precise control of electromagnetic wave propagation for various applications. This conference article explores the integration of Machine Learning (ML) algorithms to optimization of the structure to achieve enhanced performance and efficiency for Sub 6G applications (6 GHz and 2.4 GHz). The Double Square Frequency Selective Surface (DS-FSS) consists of two distinct layers of square shaped periodic patterns. This configuration pro- vides greater flexibility in tailoring the overall frequency response compared to single layer FSS. A comprehensive data set has been prepared by simulating the electromagnetic responses from the Equivalent Circuit Model (ECM) which are fed to the ML algorithm to extract the features and build the regression model and to estimate the optimal parameters. The ML algorithms used are Linear Regression and Random Forest. These are then validated against the electromagnetic simulations using HFSS Software.