Cost-effective global surrogate modeling of planar microwave filters using multi-fidelity Bayesian support vector regression

Abstract

A computationally efficient method is presented for setting up accurate Bayesian support vector regression (BSVR) models of the highly nonlinear |S21| responses of planar microstrip filters using substantially reduced finely discretized training data (compared to traditional design of experiments techniques). Inexpensive coarse-discretization full-wave simulations are exploited in conjunction with the sparseness property of BSVR to identify the regions of the input space requiring denser sampling. The proposed technique allows for substantial reduction (by up to 51%) of the computational expense necessary to collect the finely discretized training data, with negligible loss in predictive accuracy. The accuracy of the reduced-data BSVR models is confirmed by their use within a space mapping optimization algorithm