Cost-efficient behavioral modeling of antennas by means of global sensitivity analysis and dimensionality reduction

Abstract Computational tools, particularly electromagnetic (EM) solvers, are now commonplace in antenna design. While ensuring reliability, EM simulations are time-consuming, leading to high costs associated with EM-driven procedures like parametric optimization or statistical design. Various techni...

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Main Authors: Slawomir Koziel, Anna Pietrenko-Dabrowska
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
Online Access:https://doi.org/10.1038/s41598-025-87465-y
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author Slawomir Koziel
Anna Pietrenko-Dabrowska
author_facet Slawomir Koziel
Anna Pietrenko-Dabrowska
author_sort Slawomir Koziel
collection DOAJ
description Abstract Computational tools, particularly electromagnetic (EM) solvers, are now commonplace in antenna design. While ensuring reliability, EM simulations are time-consuming, leading to high costs associated with EM-driven procedures like parametric optimization or statistical design. Various techniques have been developed to address this issue, with surrogate modeling methods garnering particular attention due to their potential advantages. One key benefit is the promise of unprecedented acceleration in handling design problems that require repetitive system evaluations. However, behavioral modeling of antennas is an intrinsic endeavor. Challenges include the curse of dimensionality and the high nonlinearity of antenna characteristics. Moreover, design utility necessitates that the models are defined across wide ranges of frequency, geometry dimensions, and material parameters, posing a significant bottleneck for existing modeling frameworks. This paper introduces an innovative approach to constructing design-ready behavioral surrogates for antenna structures. Our methodology involves a rapid global sensitivity analysis (GSA) algorithm developed to determine a set of parameter space directions that maximize antenna response variability. The latter are obtained from spectral analysis of the GSA-based sensitivity indicators, and employed to define a reduced-dimensionality domain of the metamodel. The dependability of the model constructed in such a domain is superior over conventional surrogates while being suitable for design purposes. These benefits have been conclusively showcased using several microstrip antennas and illustrated by a number of design scenarios involving antenna geometry optimization for a variety of performance specifications.
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spelling doaj-art-4e0c12a72c134859b3d0e8b7980ee5202025-02-02T12:24:05ZengNature PortfolioScientific Reports2045-23222025-01-0115112110.1038/s41598-025-87465-yCost-efficient behavioral modeling of antennas by means of global sensitivity analysis and dimensionality reductionSlawomir Koziel0Anna Pietrenko-Dabrowska1Engineering Optimization & Modeling Center, Reykjavik UniversityFaculty of Electronics, Telecommunications and Informatics, Gdansk University of TechnologyAbstract Computational tools, particularly electromagnetic (EM) solvers, are now commonplace in antenna design. While ensuring reliability, EM simulations are time-consuming, leading to high costs associated with EM-driven procedures like parametric optimization or statistical design. Various techniques have been developed to address this issue, with surrogate modeling methods garnering particular attention due to their potential advantages. One key benefit is the promise of unprecedented acceleration in handling design problems that require repetitive system evaluations. However, behavioral modeling of antennas is an intrinsic endeavor. Challenges include the curse of dimensionality and the high nonlinearity of antenna characteristics. Moreover, design utility necessitates that the models are defined across wide ranges of frequency, geometry dimensions, and material parameters, posing a significant bottleneck for existing modeling frameworks. This paper introduces an innovative approach to constructing design-ready behavioral surrogates for antenna structures. Our methodology involves a rapid global sensitivity analysis (GSA) algorithm developed to determine a set of parameter space directions that maximize antenna response variability. The latter are obtained from spectral analysis of the GSA-based sensitivity indicators, and employed to define a reduced-dimensionality domain of the metamodel. The dependability of the model constructed in such a domain is superior over conventional surrogates while being suitable for design purposes. These benefits have been conclusively showcased using several microstrip antennas and illustrated by a number of design scenarios involving antenna geometry optimization for a variety of performance specifications.https://doi.org/10.1038/s41598-025-87465-yAntenna designBehavioral modelingGlobal sensitivity analysisSpectral analysisDimensionality reductionEM-driven design
spellingShingle Slawomir Koziel
Anna Pietrenko-Dabrowska
Cost-efficient behavioral modeling of antennas by means of global sensitivity analysis and dimensionality reduction
Scientific Reports
Antenna design
Behavioral modeling
Global sensitivity analysis
Spectral analysis
Dimensionality reduction
EM-driven design
title Cost-efficient behavioral modeling of antennas by means of global sensitivity analysis and dimensionality reduction
title_full Cost-efficient behavioral modeling of antennas by means of global sensitivity analysis and dimensionality reduction
title_fullStr Cost-efficient behavioral modeling of antennas by means of global sensitivity analysis and dimensionality reduction
title_full_unstemmed Cost-efficient behavioral modeling of antennas by means of global sensitivity analysis and dimensionality reduction
title_short Cost-efficient behavioral modeling of antennas by means of global sensitivity analysis and dimensionality reduction
title_sort cost efficient behavioral modeling of antennas by means of global sensitivity analysis and dimensionality reduction
topic Antenna design
Behavioral modeling
Global sensitivity analysis
Spectral analysis
Dimensionality reduction
EM-driven design
url https://doi.org/10.1038/s41598-025-87465-y
work_keys_str_mv AT slawomirkoziel costefficientbehavioralmodelingofantennasbymeansofglobalsensitivityanalysisanddimensionalityreduction
AT annapietrenkodabrowska costefficientbehavioralmodelingofantennasbymeansofglobalsensitivityanalysisanddimensionalityreduction