By Imschenetsky V.G.

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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. These advantages are normally translated into better efficiency (in particular, lower CPU cost) when physics-based surrogates are used in the design optimization process (Koziel et al.

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.

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.