A Cholesky-Based SGM-MLFMM for Stochastic Full-Wave Problems Described by Correlated Random Variables

作者：Zubac Zdravko^*; Daniel Luca; De Zutter Daniel; Ginste Dries Vande

来源：IEEE Antennas and Wireless Propagation Letters, 2017, 16: 776-779.

DOI：10.1109/LAWP.2016.2603232

• Cholesky uncertainty quantification (UQ)
• correlation scattering
• method of moments (MoM)
• multilevel fast multipole method (MLFMM)
• rough surface
• stochastic Galerkin method (SGM)

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 non-intrusive PCE methods and to the standard Monte Carlo method.

• 出版日期2017
• 单位MIT