By Slawomir Koziel
This short stories a couple of innovations exploiting the surrogate-based optimization notion and variable-fidelity EM simulations for effective optimization of antenna buildings. The creation of every approach 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 impression on functionality of the surrogate-based layout method. This quantity is appropriate for electric engineers in academia in addition to undefined, antenna designers and engineers facing computationally-expensive layout problems.
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2 Surrogate Model Construction: Function Approximation and Physics-Based… 21 few elementary ways of correcting the low-fidelity model in order to construct the physics-based surrogate. Let c(x) denote a low-fidelity model of the device of interest. One of the simplest ways of constructing a surrogate of a high-fidelity model f is a response correction. 1), particularly if the algorithm is embedded in the trust-region framework (cf. 2)), a local alignment between the surrogate and the high-fidelity model is of fundamental importance.
2)), a local alignment between the surrogate and the high-fidelity model is of fundamental importance. 12) where βi(x) = βi(x(i)) + ∇β(x(i))T(x − x(i)) and where β(x) = f(x)/c(x). , agreement of function values and their gradients at x(i) (Alexandrov and Lewis 2001). Another way of correcting the low-fidelity model is so-called input space mapping (ISM) (Bandler et al. 13) with the model parameters c(i) obtained by minimizing ||Rf(x(i)) − Rc(x(i) + c(i))||. 5 shows an example of a filter structure evaluated using EM simulation (high-fidelity model), its circuit equivalent (low-fidelity model), and the corresponding |S21| responses before and after applying the ISM correction.
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. 1994, 2004a, b). , the output SM of the form Rs ( x , p ) = Rs ( x , d ) = Rc ( x ) + d or Rs ( x, p ) = Rs ( x, A) = A i Rc ( x ) (Bandler et al. 2003; Koziel et al. 2006). f (x,F) F = [ f 1 f 2]T Fig. 1 Basic space mapping surrogate model types: (a) input SM, (b) output SM, (c) implicit SM, (d) frequency SM high-fidelity model but can be freely altered in the low-fidelity model (Bandler et al.