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>Cost-effective global surrogate modeling of planar microwave filters using multi-fidelity Bayesian support vector regression
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Cost-effective global surrogate modeling of planar microwave filters using multi-fidelity Bayesian support vector regression
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机译:使用多保真贝叶斯支持向量回归的平面微波滤波器的经济有效的全局替代建模
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摘要
A computationally efficient method is presented for setting up accurate Bayesian support vector regression (BSVR) models of the highly nonlinear |S21| responses of planar microstrip filters using substantially reduced finely discretized training data (compared to traditional design of experiments techniques). Inexpensive coarse-discretization full-wave simulations are exploited in conjunction with the sparseness property of BSVR to identify the regions of the input space requiring denser sampling. The proposed technique allows for substantial reduction (by up to 51%) of the computational expense necessary to collect the finely discretized training data, with negligible loss in predictive accuracy. The accuracy of the reduced-data BSVR models is confirmed by their use within a space mapping optimization algorithm
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