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Antenna Modeling Using Variable-Fidelity EM Simulations and Constrained Co-Kriging

Antenna Modeling Using Variable-Fidelity EM Simulations and Constrained Co-Kriging


Title: Antenna Modeling Using Variable-Fidelity EM Simulations and Constrained Co-Kriging
Author: Pietrenko-Dabrowska, Anna
Koziel, Slawomir   orcid.org/0000-0002-0584-4427
Date: 2020-05-27
Language: English
Scope: 91048-91056
University/Institute: Háskólinn í Reykjavík
Reykjavik University
School: Tæknisvið (HR)
School of Technology (RU)
Department: Verkfræðideild (HR)
Department of Engineering (RU)
Engineering Optimization and Modeling Center (EOMC)
Series: IEEE Access;8
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2020.2993951
Subject: General Engineering; General Materials Science; General Computer Science; Antenna design; Surrogate modeling; Kriging interpolation; Co-kriging; Electromagnetic (EM) simulation; Verkfræði; Efnisfræði; Tölvunarfræði; Loftnet; Hönnun; Rafsegulfræði
URI: https://hdl.handle.net/20.500.11815/2268

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Citation:

A. Pietrenko-Dabrowska and S. Koziel, “Antenna Modeling Using Variable-Fidelity EM Simulations and Constrained Co-Kriging,” IEEE Access, vol. 8, pp. 91048–91056, 2020, doi: 10.1109/ACCESS.2020.2993951

Abstract:

Utilization of fast surrogate models has become a viable alternative to direct handling of full-wave electromagnetic (EM) simulations in EM-driven design. Their purpose is to alleviate the difficulties related to high computational cost of multiple simulations required by the common numerical procedures such as parametric optimization or uncertainty quantification. Yet, conventional data-driven (or approximation) modeling techniques are severely affected by the curse of dimensionality. This is a serious limitation when it comes to modeling of highly nonlinear antenna characteristics. In practice, general-purpose surrogates can be rendered for the structures described by a few parameters within limited ranges thereof, which is grossly insufficient from the utility point of view. This paper proposes a novel modeling approach involving variable-fidelity EM simulations incorporated into the recently reported nested kriging modeling framework. Combining the information contained in the densely sampled low- and sparsely sampled high-fidelity models is realized using co-kriging. The resulting surrogate exhibits the predictive power comparable to the model constructed using exclusively high-fidelity data while offering significantly reduced setup cost. The advantages over conventional surrogates are pronounced even further. The presented modeling procedure is demonstrated using two antenna examples and further validated through the application case studies.

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This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

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