Multilevel Domain Uncertainty Quantification in Computational Electromagnetics

TL;DR

This paper develops and analyzes multilevel Monte Carlo and sparse-grid methods for quantifying electromagnetic field uncertainties in complex geometries, achieving dimension-independent convergence and efficient computation.

Contribution

It introduces a rigorous framework for uncertainty quantification in electromagnetics using multilevel and sparse-grid techniques with proven convergence rates.

Findings

Dimension-independent algebraic convergence achieved.

Sparse-grid methods outperform Monte Carlo in efficiency.

Numerical results confirm theoretical convergence rates.

Abstract

We continue our study [Domain Uncertainty Quantification in Computational Electromagnetics, JUQ (2020), 8:301--341] of the numerical approximation of time-harmonic electromagnetic fields for the Maxwell lossy cavity problem for uncertain geometries. We adopt the same affine-parametric shape parametrization framework, mapping the physical domains to a nominal polygonal domain with piecewise smooth maps. The regularity of the pullback solutions on the nominal domain is characterized in piecewise Sobolev spaces. We prove error convergence rates and optimize the algorithmic steering of parameters for edge-element discretizations in the nominal domain combined with: (a) multilevel Monte Carlo sampling, and (b) multilevel, sparse-grid quadrature for computing the expectation of the solutions with respect to uncertain domain ensembles. In addition, we analyze sparse-grid interpolation to compute surrogates of the domain-to-solution mappings. All calculations are performed on the polyhedral nominal domain, which enables the use of standard simplicial finite element meshes. We provide a rigorous fully discrete error analysis and show, in all cases, that dimension-independent algebraic convergence is achieved. For the multilevel sparse-grid quadrature methods, we prove higher order convergence rates which are free from the so-called curse of dimensionality, i.e. independent of the number of parameters used to parametrize the admissible shapes. Numerical experiments confirm our theoretical results and verify the superiority of the sparse-grid methods.