By Slawomir Koziel

This short reports a couple of strategies exploiting the surrogate-based optimization suggestion and variable-fidelity EM simulations for effective optimization of antenna buildings. The creation of every process is illustrated with examples of antenna layout. The authors display the ways that practitioners can receive an optimized antenna layout on the computational fee reminiscent of a number of high-fidelity EM simulations of the antenna constitution. there's additionally a dialogue of the choice of antenna version constancy and its effect on functionality of the surrogate-based layout strategy. This quantity is acceptable for electric engineers in academia in addition to undefined, antenna designers and engineers facing computationally-expensive layout problems.

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2004a, b). In case of antennas, the only universally available way of obtaining low-fidelity models is through coarse-discretization EM simulation. A discussion of low-fidelity antenna models is presented in Chap. 5 of this book. The main advantage of physics-based models is that—because of exploiting some knowledge embedded in the low-fidelity model—a limited amount of high-­ fidelity data is necessary to ensure decent accuracy. , they can provide reliable prediction of the high-fidelity model response at the designs not used in the training process.

2005). Allocation of the base points is usually executed using Latin hypercube sampling (Beachkofski and Grandhi 2002). Steps 6–12 describe the flow of the SM algorithm. 1 shows the flowchart of the design process. 3 Shape-Preserving Response Prediction Shape-preserving response prediction (SPRP) (Koziel 2010a) is one of the recent SBO techniques that exploit the knowledge embedded in the low-fidelity model in order to predict the high-fidelity model response. Unlike space mapping, SPRP does not use any extractable parameters, and, therefore, it is more suitable for antenna design.

The first step of the ASM algorithm is to find xc*. , P(xf(j)) = arg min{xc: Rf(xf(j)) − Rf(xc)||}. 5) where B(j) is the approximation of the space mapping Jacobian JP = JP(xf) = [∂PT/∂xf]T = [∂(xcT)/∂xf]T. 6) The algorithm terminates if ||f(j)|| is sufficiently small. The output of the algorithm is an approximation to xf# = P−1(xc*). 7) Several variations of ASM have been considered in the literature, including hybrid ASM (Bakr et al. 1999) and trust-region ASM (Bakr et al. 1998). 3). 1). 13) shows a simple example of a so-called input SM surrogate that was the first parameterized version of space mapping (Bandler et al.

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