Abstract:
Bayesian support vector regression (BSVR) modeling of
planar 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 fine-discretization training
points for establishing a high-fidelity BSVR model of the antenna. As
demonstrated using three planar antennas with different response types,
the proposed technique allows substantial reduction (up to 48%) of the
computational effort necessary to set up the fine-discretization training
data sets for the high-fidelity models with negligible loss in predictive
power. The accuracy of the reduced-data BSVR models is confirmed by
their successful use within a space mapping optimization/design algorithm.
