2019
DOI: 10.1108/ec-04-2019-0155
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Expedited antenna optimization with numerical derivatives and gradient change tracking

Abstract: Purpose The purpose of this study is to propose a framework for expedited antenna optimization with numerical derivatives involving gradient variation monitoring throughout the optimization run and demonstrate it using a benchmark set of real-world wideband antennas. A comprehensive analysis of the algorithm performance involving multiple starting points is provided. The optimization results are compared with a conventional trust-region (TR) procedure, as well as the state-of-the-art accelerated TR algorithms.… Show more

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Cited by 24 publications
(12 citation statements)
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“…Owing to the extrapolation techniques described above, the initial designs are normally of good quality so local tuning is sufficient. Here, it is realized using an accelerated version of the trust-region (TR) gradient-search proposed in 40 . The algorithm is briefly outlined here for the convenience of the reader.…”
Section: Local Tuning Algorithmmentioning
confidence: 99%
See 1 more Smart Citation
“…Owing to the extrapolation techniques described above, the initial designs are normally of good quality so local tuning is sufficient. Here, it is realized using an accelerated version of the trust-region (TR) gradient-search proposed in 40 . The algorithm is briefly outlined here for the convenience of the reader.…”
Section: Local Tuning Algorithmmentioning
confidence: 99%
“…This is realized using inverse modeling methods 38 and extrapolation. Furthermore, the low cost of the parameter tuning process is ensured by utilization of trust-region gradient search with sparse sensitivity updates 39 , 40 . The proposed technique is demonstrated using three examples of wide-slot CP structures.…”
Section: Introductionmentioning
confidence: 99%
“…Using U, the parameter tuning problem can be formulated as a nonlinear minimization task of the form x*=U*bold-italicRbold-italicx,bold-italicF=arg0.5em0em0emfalseminxUbold-italicRbold-italicx,bold-italicF, where x * is the optimum design to be found. The problem (2) may be subject to additional constraints, which can be handled explicitly or implicitly (e.g., using the penalty function approach 55 ).…”
Section: Expedited Antenna Parameter Tuning Using Inverse/forward Surrogates and Response Featuresmentioning
confidence: 99%
“…Improving computational efficiency of simulation-based design procedures has been targeted by numerous research endeavours. These efforts focused on the development of strictly algorithmic approaches, both intrusive (e.g., gradient-based procedures accelerated by means of adjoint sensitivities 27 , 28 ), and non-intrusive (e.g., trust-region methods with sparse sensitivity updates 29 , 30 , as well as surrogate-based frameworks involving data-driven 7 , and physics-based metamodels 5 . Although approximation surrogates (kriging 31 , radial-basis functions 32 , support vector regression 33 , polynomial chaos expansion 34 , 35 , neural networks 36 , Gaussian process regression 37 , polynomial regression 38 ) are by far more popular, their application is limited by the curse of dimensionality, which is particularly troublesome when handling nonlinear outputs of high-frequency structures.…”
Section: Introductionmentioning
confidence: 99%