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

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3 Author(s)
Jacobs, J.P. ; Dept. of Electr., Electron. & Comput. Eng., Univ. of Pretoria, Pretoria, South Africa ; Koziel, S. ; Ogurtsov, S.

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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Antennas and Propagation, IEEE Transactions on  (Volume:61 ,  Issue: 2 )