A Cholesky-Based SGM-MLFMM for Stochastic Full-Wave Problems Described by Correlated Random Variables
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Author(s)
Zubac, Zdravko
De Zutter, Daniel
Vande Ginste, Dries
Daniel, Luca
Date Issued
August 2016
Journal
IEEE Antennas and Wireless Propagation Letters
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Zubac, Zdravko et al. “A Cholesky-Based SGM-MLFMM for Stochastic Full-Wave Problems Described by Correlated Random Variables.” IEEE Antennas and Wireless Propagation Letters 16 (2017): 776–779.
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Author's final manuscript
Abstract
In this letter, the multilevel fast multipole method (MLFMM) is combined with the polynomial chaos expansion (PCE)-based stochastic Galerkin method (SGM) to stochastically model scatterers with geometrical variations that need to be described by a set of correlated random variables (RVs). It is demonstrated how Cholesky decomposition is the appropriate choice for the RVs transformation, leading to an efficient SGM-MLFMM algorithm. The novel method is applied to the uncertainty quantification of the currents induced on a rough surface, being a classic example of a scatterer described by means of correlated RVs, and the results clearly demonstrate its superiority compared to the nonintrusive PCE methods and to the standard Monte Carlo method.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.1109/LAWP.2016.2603232
