Bayesian Support Vector Regression Modeling of Microwave Structures for Design Applications

Fast and accurate models are indispensable in contemporary microwave engineering. Kernel-based machine learning methods applied to the modeling of microwave structures have recently attracted substantial attention; these include support vector regression and Gaussian process regression. Among them, Bayesian support vector regression (BSVR) with automatic relevance determination (ARD) proved to perform particularly well when modeling input characteristics of microwave devices. In this chapter, we apply BSVR to the modeling of microwave antennas and filters. Moreover, we discuss a more efficient version of BSVR-based modeling exploiting variable-fidelity electromagnetic (EM) simulations, where coarse-discretization EM simulation data is used to find a reduced number of fine-discretization training points for establishing a high-fidelity BSVR model of the device of interest. We apply the BSVR models to design optimization. In particular, embedding the BSVR model obtained from coarse-discretization EM data into a surrogate-based optimization framework exploiting space mapping allows us to yield an optimized design at a low computational cost corresponding to a few evaluations of the high-fidelity EM model of the considered device. The presented techniques are illustrated using several examples of antennas and microstrip filters.