By Slawomir Koziel
This short reports a couple of innovations exploiting the surrogate-based optimization notion and variable-fidelity EM simulations for effective optimization of antenna constructions. The advent 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 resembling a couple of high-fidelity EM simulations of the antenna constitution. there's additionally a dialogue of the choice of antenna version constancy and its impression on functionality of the surrogate-based layout method. 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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Extra resources for Antenna Design by Simulation-Driven Optimization
9) Here, wi·k are weighting factors; a common choice of wi·k is wi·k = 1 for all i and all k (all previous designs contribute to the parameter extraction process) or wi·1 = 1 and wi·k = 0 for k < i (the surrogate model depends on the most recent design only). 13). 11) is a vector of model parameters. , Bandler et al. 2004a, b and Koziel et al. 2006). They can be roughly divided into four categories: • Models based on a distortion of the low-fidelity model parameter space, in particular, the input SM of the form Rs ( x, p ) = Rs ( x, B, C ) = Rc ( B i x + c ) (Bandler et al.
The optimum of Rcd is usually the best design we can get at a reasonably low computational cost. This cost can be further reduced by relaxing tolerance requirements while searching for x(0): due to a limited accuracy of Rcd, it is sufficient to find only a rough approximation of its optimum. Steps 3–5 describe the construction of the RSA coarse model. A popular choice of RSA is kriging interpolation (Queipo et al. 2005). Allocation of the base points is usually executed using Latin hypercube sampling (Beachkofski and Grandhi 2002).
13) More information about physics-based surrogates can be found in Chap. 4. It should be emphasized that simple correction schemes such as those described here are often used as building blocks to construct more involved surrogate models (Koziel et al. 2006). 95 Fig. 3 Exploration Versus Exploitation The surrogate-based optimization process starts from an initial surrogate model which is updated using the high-fidelity model data that is accumulated in the optimization process. In particular, the high-fidelity model has to be evaluated for verification at any new design x(i) provided by the surrogate model.