Gaussian process antenna modeling using neighborhood-data-expanded training sets

A cost-effective enhancement to the training of Gaussian process regression (GPR) models of microwave antenna (and other) structures is presented. In particular, we investigate improving GPR accuracy by employing additional training points that may typically be generated through sensitivity analysis, entailing negligible computational cost compared to obtaining additional data through full-wave simulations. We demonstrate, using two examples, that significant reduction of the modeling error is possible even though the location of the additional training points is constrained to the vicinity of the original training locations.