Reduced-cost Bayesian support vector regression modeling and optimization of planar slot antennas

Bayesian support vector regression (BSVR) modeling of coplanar waveguide-fed slot antennas with reduced training sets for computational efficiency is presented. Coarse-discretization electromagnetic (EM) simulations are exploited in order to find a reduced number of training points used to establish a high-fidelity BSVR model of the antenna. As demonstrated using two antenna examples, the proposed technique allows substantial reduction (up to 48%) of the computational effort necessary to set up the high-fidelity models as compared to conventional approximation-based models, with negligible loss in accuracy. Application of the reduced BSVR models to antenna design is demonstrated.