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Technologies 2019, 7(2), 37; https://doi.org/10.3390/technologies7020037

Efficient Uncertainty Assessment in EM Problems via Dimensionality Reduction of Polynomial-Chaos Expansions

1
Department of Informatics and Telecommunications Engineering, University of Western Macedonia, Kozani 50131, Greece
2
Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in the Proceedings of the 7th International Conference on Modern Circuits and Systems Technologies (MOCAST2018) on Electronics and Communications, Thessaloniki, Greece, 7–9 May 2018.
Received: 31 January 2019 / Revised: 11 April 2019 / Accepted: 15 April 2019 / Published: 17 April 2019
(This article belongs to the Special Issue Modern Circuits and Systems Technologies on Communications)
PDF [1361 KB, uploaded 17 April 2019]

Abstract

The uncertainties in various Electromagnetic (EM) problems may present a significant effect on the properties of the involved field components, and thus, they must be taken into consideration. However, there are cases when a number of stochastic inputs may feature a low influence on the variability of the outputs of interest. Having this in mind, a dimensionality reduction of the Polynomial Chaos (PC) technique is performed, by firstly applying a sensitivity analysis method to the stochastic inputs of multi-dimensional random problems. Therefore, the computational cost of the PC method is reduced, making it more efficient, as only a trivial accuracy loss is observed. We demonstrate numerical results about EM wave propagation in two test cases and a patch antenna problem. Comparisons with the Monte Carlo and the standard PC techniques prove that satisfying outcomes can be extracted with the proposed dimensionality-reduction technique.
Keywords: Monte Carlo method; Morris method; polynomial chaos; random media problems; variable screening Monte Carlo method; Morris method; polynomial chaos; random media problems; variable screening
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Salis, C.; Kantartzis, N.; Zygiridis, T. Efficient Uncertainty Assessment in EM Problems via Dimensionality Reduction of Polynomial-Chaos Expansions. Technologies 2019, 7, 37.

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