By Slawomir Koziel, Stanislav Ogurtsov
This short experiences a few recommendations exploiting the surrogate-based optimization inspiration and variable-fidelity EM simulations for effective optimization of antenna constructions. The creation of every approach is illustrated with examples of antenna layout. The authors exhibit the ways that practitioners can receive an optimized antenna layout on the computational rate 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 impact on functionality of the surrogate-based layout technique. This quantity is acceptable for electric engineers in academia in addition to undefined, antenna designers and engineers facing computationally-expensive layout difficulties.
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Additional resources for Antenna Design by Simulation-Driven Optimization
Not long time ago, discrete EM simulators were used mostly for design verification purposes. Nowadays, due to the progress in computing hardware as well as development of computational electromagnetic methods, the discrete EM simulators turn to be indispensable for the entire design process starting from a concept estimation step. The use of discrete full-wave simulators is also appealing from the practical point of view and allows obtaining reliable antenna responses with respect to environment and feeds.
When properly set up, coarse-discretization low-fidelity models are sufficiently accurate to be used by SBO-based design procedures. However, they are also relatively expensive, typically, only 10–50 times faster than respective highfidelity models. This creates specific challenges while developing SBO techniques for antenna design. In particular, the total evaluation cost of the low-fidelity model in the SBO process (both due to updating and optimization of the surrogate model) cannot be neglected and can significantly contribute to the overall design cost.
This is usually justified because the initial design is assumed to be reasonably good (in practice, it is obtained through local or global optimization of the low-fidelity model). This choice of infill criteria aims at the exploitation of a certain region of the design space, more specifically, vicinity of a local optimum that is close to the initial design. The exploration of the design space implies in most cases a global search. If the underlying objective function is non-convex, exploration usually boils down to performing a global sampling of the search space, for example, by selecting those points that maximize some estimation of the error associated to the surrogate considered (Forrester and Keane 2009).
Antenna Design by Simulation-Driven Optimization by Slawomir Koziel, Stanislav Ogurtsov