Two-Stage Gaussian Process Modeling of Microwave Structures for Design Optimization

Accurate models that can be rapidly evaluated are indispensable in microwave engineering. Kernel-based machine learning methods applied to the modeling of microwave structures have recently attracted attention; these include support vector regression, Bayesian support vector regression, and Gaussian process regression (GPR). In this chapter, we apply an extended methodology based on GPR, namely two-stage GPR, to the modeling of microwave antennas and filters. At the core of the method lies variable-fidelity electromagnetic simulations. In the first stage, a mapping between electromagnetic models (simulations) of low and high fidelity is learned, which allows for significantly reducing the computational effort necessary to set up the high-fidelity training data sets for the actual surrogate models (second stage), with negligible loss in predictive power. We apply the two-stage models to design optimization involving several examples of antennas and microstrip filters.