Accelerated multiobjective design of miniaturized microwave components by means of nested kriging surrogates

Pietrenko‐Dabrowska, Anna; Koziel, Slawomir

doi:10.1002/mmce.22124

Cited by 26 publications

(15 citation statements)

References 71 publications

(129 reference statements)

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“…6 shows the results of MO design of the 15-parameter impedance matching transformer optimized with respect to two objectives: reduction of the circuit footprint and improvement of the in-band matching. Table 2 makes a comparison of MO cost for the framework of [148] and the technique exploiting the nested-Kriging surrogate [153]. Over sixty-percent cost reduction is observed when the surrogate model constructed in the initially-reduced space is replaced by the nested-Kriging surrogate of [153], which gives a better account for the Pareto front geometry.…”

Section: Simulation-driven Surrogate-assisted Multi-objective Desmentioning

confidence: 99%

“…Table 2 makes a comparison of MO cost for the framework of [148] and the technique exploiting the nested-Kriging surrogate [153]. Over sixty-percent cost reduction is observed when the surrogate model constructed in the initially-reduced space is replaced by the nested-Kriging surrogate of [153], which gives a better account for the Pareto front geometry. It seems that the employment of fast surrogate models is a prerequisite for computationally-efficient EM-driven MO design.…”

Section: Simulation-driven Surrogate-assisted Multi-objective Desmentioning

confidence: 99%

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Advanced RF and Microwave Design Optimization: A Journey and a Vision of Future Trends

2021

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Section: Simulation-driven Surrogate-assisted Multi-objective Desmentioning

confidence: 99%

Section: Simulation-driven Surrogate-assisted Multi-objective Desmentioning

confidence: 99%

Advanced RF and Microwave Design Optimization: A Journey and a Vision of Future Trends

2021

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“…Consider a compact three-section 50-to-100 Ohm impedance matching transformer of [58]. The circuit geometry has been shown in Fig.…”

Section: A Case 1: Three-section Cmrc-based Impedance Matching Transmentioning

confidence: 99%

Design-Oriented Two-Stage Surrogate Modeling of Miniaturized Microstrip Circuits With Dimensionality Reduction

2020

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Contemporary microwave design heavily relies on full-wave electromagnetic (EM) simulation tools. This is especially the case for miniaturized devices where EM cross-coupling effects cannot be adequately accounted for using equivalent network models. Unfortunately, EM analysis incurs considerable computational expenses, which becomes a bottleneck whenever multiple evaluations are required. Common simulation-based design tasks include parametric optimization and uncertainty quantification. These can be accelerated using fast replacement models, among which the data-driven surrogates are the most popular. Notwithstanding, a construction of approximation models for microwave components is hindered by the dimensionality issues as well as high nonlinearity of system characteristics. A partial alleviation of the mentioned difficulties can be achieved with the recently reported performance-driven modeling methods, including the nested kriging framework. Therein, the computational benefits are obtained by appropriate confinement of the surrogate model domain, spanned by a set of pre-optimized reference designs, and by focusing on the parameter space region that contains high quality designs with respect to the considered performance figures. This paper presents a methodology that incorporates the concept of nested kriging and enhances it by explicit dimensionality reduction based on spectral decomposition of the reference design set. Extensive verification studies conducted for a compact rat-race coupler and a three-section impedance matching transformer demonstrate superiority of the presented approach over both the conventional techniques and the nested kriging in terms of modeling accuracy. Design utility of our surrogates is corroborated through application cases studies. INDEX TERMS Microwave design; compact circuits; surrogate modeling; domain confinement; principal component analysis; dimensionality reduction.

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“…More involved reduction techniques may provide even better confinement. An example is the parameter space reduction by means of nested kriging [68].…”

Section: B Initial Parameter Space Confinementmentioning

confidence: 99%

Fast Multi-Objective Optimization of Antenna Structures by Means of Data-Driven Surrogates and Dimensionality Reduction

Koziel

Pietrenko‐Dabrowska

2020

IEEE Access

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Design of contemporary antenna structures needs to account for several and often conflicting objectives. These are pertinent to both electrical and field properties of the antenna but also its geometry (e.g., footprint minimization). For practical reasons, especially to facilitate efficient optimization, single-objective formulations are most often employed, through either a priori preference articulation, objective aggregation, or casting all but one (primary) objective into constraints. Notwithstanding, the knowledge of the best possible design trade-offs provides a more comprehensive insight into the properties of the antenna structure at hand. Genuine multi-objective optimization is a proper way of acquiring such data, typically rendered in the form of a Pareto set that represents the mentioned trade-off solutions. In antenna design, the fundamental challenge is high computational cost of multi-objective optimization, normally carried out using populationbased metaheuristic algorithms. In most practical cases, the use of reliable, yet costly, full-wave electromagnetic models is imperative to ensure evaluation reliability, which makes conventional multiobjective optimization procedures prohibitively expensive. The employment of fast surrogates (or metamodels) can alleviate these difficulties, yet, construction of metamodels faces considerable challenges by itself, mostly related to the curse of dimensionality. This work proposes a novel surrogate-assisted approach to multi-objective optimization, where the data-driven model is only rendered in a small region spanned by the selected principal components of the extreme Pareto-optimal design set obtained using local search routines. The region is limited in terms of parameter ranges but also dimensionality, yet contains the majority of Pareto front, therefore surrogate construction therein does not incur considerable costs. The typical cost of identifying the Pareto set is just a few hundred of full-wave analyses of the antenna under design. Our technique is validated using two antenna examples (a planar Yagi and an ultra-wideband monopole antenna) and favorably compared to state-of-the-art surrogate-assisted multi-objective optimization methods. INDEX TERMS Antenna design; multi-objective optimization; surrogate modeling; domain confinement; principal components; dimensionality reduction.

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Accelerated multiobjective design of miniaturized microwave components by means of nested kriging surrogates

Cited by 26 publications

References 71 publications

Advanced RF and Microwave Design Optimization: A Journey and a Vision of Future Trends

Advanced RF and Microwave Design Optimization: A Journey and a Vision of Future Trends

Design-Oriented Two-Stage Surrogate Modeling of Miniaturized Microstrip Circuits With Dimensionality Reduction

Fast Multi-Objective Optimization of Antenna Structures by Means of Data-Driven Surrogates and Dimensionality Reduction

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