Computationally efficient multi-fidelity Bayesian support vector regression modeling of planar antenna input characteristics

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Authors

Jacobs, Jan Pieter
Koziel, S.
Ogurtsov, S.

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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.

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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.