By Slawomir Koziel, Stanislav Ogurtsov

This short experiences a few suggestions exploiting the surrogate-based optimization thought and variable-fidelity EM simulations for effective optimization of antenna constructions. The advent of every technique is illustrated with examples of antenna layout. The authors show the ways that practitioners can receive an optimized antenna layout on the computational rate 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 impact on functionality of the surrogate-based layout strategy. This quantity is appropriate for electric engineers in academia in addition to undefined, antenna designers and engineers facing computationally-expensive layout difficulties.

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**Extra resources for Antenna Design by Simulation-Driven Optimization**

**Sample text**

Therefore, the use of the low-fidelity model in the process of antenna adjustment can result in high reflection of a manufactured sample around 5 GHz, whereas the use of the low-fidelity model will be associated with a substantial total design time. Other figures of the antennas are not so sensitive to the model fidelity, as illustrated in Fig. 2c and d where we see no essential differences of the gain patterns of the two models. 7 c d 30 60 60 90 10 0 47 −10 −20 [dB] −20 −10 0 90 10 30 0 30 60 90 10 60 0 −10 −20 [dB] −20 −10 0 90 10 Fig.

11 illustrates an iteration of the procedure used for design of a CBCPW-to-SIW transition (Koziel 2011). 7 Multi-fidelity Design Optimization b 0 −10 |S11|, |S22| |S11|, |S22| a 41 −20 −30 8 10 Frequency [GHz] −20 12 d 0 −10 |S11|, |S22| |S11|, |S22| −10 −30 6 c 0 −20 −30 6 8 10 Frequency [GHz] 12 6 8 10 Frequency [GHz] 12 0 −10 −20 −30 6 8 10 Frequency [GHz] 12 Fig. 11 Adaptively adjusted design specification technique applied to optimize CBCPW-to- SIW transitions (Koziel 2011). Rf and Rc responses are denoted as solid and dashed lines, respectively.

Set i = 0. 7. Evaluate the fine model Rf at x(i). 8. 11). 9. 1). 10. Set i = i + 1. 11. If the termination condition is not satisfied, go to 7. 12. End. The first phase of the design process is to find an optimized design of the coarse-discretization model. 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.