Low-cost design optimization of slot antennas using Bayesian support vector regression and space mapping

Abstract

A computationally efficient procedure for design optimization of slot antennas is presented. We use space mapping as the main optimization engine, the underlying coarse model being coarse-discretization electromagnetic (EM) simulation data of the antenna structure of interest (low-fidelity model). In order to speed up the design process, the low-fidelity model is not used directly in the process; instead, the coarse-discretization simulation data - sampled only in the vicinity of their approximate optimum - are used to create an auxiliary response surface model through Bayesian support vector regression. The latter - after suitable space-mapping-based correction - serves as a prediction tool to find an accurate optimum design of the antenna. The proposed procedure is illustrated using two examples of slot antennas.