Jacobs, Jan PieterKoziel, S.Ogurtsov, S.2016-12-092016-12-092013Jacobs, JP, Koziel, S & Ogurtsov, S. 2013, 'Computationally efficient multi-fidelity Bayesian support vector regression modeling of planar antenna input characteristics', IEEE Transactions on Antennas and Propagation, vol. 61, no. 2, pp. 980-984.0018-926X10.1109/TAP.2012.2220513http://hdl.handle.net/2263/58368Bayesian 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.en© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists of any copyrighted components of this work in other works.Gaussian processesMicrowave antennasOptimizationPredictive modelsSupport vector machinesBayesian support vector regression (BSVR)Planar antennasCoarse-discretization electromagnetic (EM) simulationsPostprint Article