Computationally Efficient Multi-Fidelity Bayesian Support Vector Regression Modeling of Planar Antenna Input Characteristics

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.

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