Computationally Efficient Multi-Fidelity Bayesian Support Vector Regression Modeling of Planar Antenna Input Characteristics

Jacobs, J. Pieter; Koziel, Slawomir; Ogurtsov, Stanislav

doi:10.1109/tap.2012.2220513

Cited by 31 publications

(11 citation statements)

References 18 publications

(23 reference statements)

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“…Slots antennas were optimized in References 194 and 195 by using Space Mapping as an optimization engine. Computational costs were reduced by implementing Bayesian SVR (BSVR) 196 as the coarse response surface model instead of relying on electromagnetic simulations.…”

Section: Machine Learning‐assisted Antenna Optimizationmentioning

confidence: 99%

A review on the design and optimization of antennas using machine learning algorithms and techniques

Misilmani

Naous

Khatib

2020

Int J RF Microw Comput Aided Eng

102

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This paper presents a focused and comprehensive literature survey on the use of machine learning (ML) in antenna design and optimization. An overview of the conventional computational electromagnetics and numerical methods used to gain physical insight into the design of the antennas is first presented. The major aspects of ML are then presented, with a study of its different learning categories and frameworks. An overview and mathematical briefing of regression models built with ML algorithms is then illustrated, with a focus on those applied in antenna synthesis and analysis. An in‐depth overview on the different research papers discussing the design and optimization of antennas using ML is then reported, covering the different techniques and algorithms applied to generate antenna parameters based on desired radiation characteristics and other antenna specifications. Various investigated antennas are sorted based on antenna type and configuration to assist the readers who wish to work with a specific type of antennas using ML.

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Section: Machine Learning‐assisted Antenna Optimizationmentioning

confidence: 99%

A review on the design and optimization of antennas using machine learning algorithms and techniques

Misilmani

Naous

Khatib

2020

Int J RF Microw Comput Aided Eng

102

View full text Add to dashboard Cite

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“…Whereas the extraction of the coupling matrix is difficult when the detuning is high. In addition, Bayesian SVR have been applied to the modeling of plannar antennas and microwave filters, which aims to reduce training sets and establishes a high‐fidelity BSVR model . Reference presents a hybrid modeling approach to solve problem of the deficiency of measured data and improve modeling accuracy.…”

Section: Introductionmentioning

confidence: 99%

Parametric modeling of microwave filter using combined MLS-SVR and pole-residue-based transfer functions

Cao

Liu

Yuan

et al. 2018

Int J RF Microw Comput Aided Eng

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In this article, an advanced technique is developed to combine multi‐output least‐squares support vector regression (MLS‐SVR) and pole‐residue‐based transfer function models for microwave filter parametric modeling. MLS‐SVR is trained to learn the relationship between the length of tuning screws and the pole/residues of the transfer function, where MLS‐SVR is an effective method to cope with the multi‐output case unlike the traditional approach. Traditional approach treats the different outputs separately in the multi‐output case and it cannot model the relation between different outputs. Another important element for modeling is feature parameters. Extracted feature parameters have an important influence on the accuracy of modeling. For the purpose of establishing a more accurate model, the complex system poles and residues from Y‐parameters are chosen as the outputs of modeling, which are obtained by vector fitting (VF). Then we give a solution to obtaining pole/residues extracted by VF when the filter is in high detuned. After the proposed modeling process, trained model can be used to provide an accurate and fast prediction of the behavior of microwave filter with the length of tuning screws as variables, and model the electromagnetic simulation (or actual) microwave filter tuning. The methodology is applied to a narrow band coaxial‐resonator filter modeling, and more accurate results are achieved compared with the other methods. An example is presented to illustrate the efficiency of the proposed method.

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“…It is important to highlight that this method benefits from good computational characteristics of Bayesian approaches such as having information about the uncertainties of the estimation parameters (through their prior and updated distributions). In [13], the authors have applied a multifidelity Bayesian support vector regression to surrogate the input of planar antenna. To the authors' knowledge, this work is the first contribution that applies a Co-Kriging based Bayesian multifidelity framework to electromagnetics.…”

Section: Introductionmentioning

confidence: 99%

Efficient Bayesian Multifidelity Approach in Metamodeling of an Electromagnetic Simulator Response

Bdour¹,

Guiffaut²,

Reineix³

2017

PIER M

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Abstract-Several computer codes with varying accuracy from rigorous full-wave methods (highfidelity models) to less accurate Transmission Line (TL) approaches (low-fidelity model) have been proposed to solve EMC problems of interference between parasitic waves and wired communication systems. For solving engineering tasks, with a limited computational budget, we need to build surrogate models of high-fidelity (HF) computer codes. However, one of their main limitations is their expensive computational time. Rather than using only the computationally costly HF simulations, we apply another type of surrogate models, called Multifidelity (MF) metamodel which efficiently combines, within a Bayesian framework, high and low-fidelity (LF) evaluations to speed up the surrogate model building. The numerical results of combination of an expensive EMC simulator and a cheap TL code to solve a plane wave illumination problem, show that, compared to Kriging, a reliable Bayesian MF metamodel of equivalent or higher predictivity can be obtained within less simulation time.

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Computationally Efficient Multi-Fidelity Bayesian Support Vector Regression Modeling of Planar Antenna Input Characteristics

Cited by 31 publications

References 18 publications

A review on the design and optimization of antennas using machine learning algorithms and techniques

A review on the design and optimization of antennas using machine learning algorithms and techniques

Parametric modeling of microwave filter using combined MLS-SVR and pole-residue-based transfer functions

Efficient Bayesian Multifidelity Approach in Metamodeling of an Electromagnetic Simulator Response

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