Computationally efficient multi-fidelity Bayesian support vector regression modeling of planar antenna input characteristics
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Date
Authors
Jacobs, Jan Pieter
Koziel, S.
Ogurtsov, S.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers
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.
Description
Keywords
Gaussian processes, Microwave antennas, Optimization, Predictive models, Support vector machines, Bayesian support vector regression (BSVR), Planar antennas, Coarse-discretization electromagnetic (EM) simulations
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Citation
Jacobs, 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.