A Dimension-adaptive Combination Technique for Uncertainty Quantification

TL;DR

This paper introduces an adaptive sparse grid algorithm for efficiently computing quantities of interest in stochastic elliptic PDEs with high-dimensional parametric spaces, without prior knowledge of coefficient decay.

Contribution

It proposes a dimension-adaptive combination technique that balances spatial, parametric, and stochastic approximations, adapting to anisotropy and regularity.

Findings

Effective in handling high-dimensional parametric spaces.

Achieves near-optimal convergence rates for smooth random fields.

Demonstrates good performance on Darcy problem with lognormal permeability.

Abstract

We present an adaptive algorithm for the computation of quantities of interest involving the solution of a stochastic elliptic PDE where the diffusion coefficient is parametrized by means of a Karhunen-Lo\`eve expansion. The approximation of the equivalent parametric problem requires a restriction of the countably infinite-dimensional parameter space to a finite-dimensional parameter set, a spatial discretization and an approximation in the parametric variables. We consider a sparse grid approach between these approximation directions in order to reduce the computational effort and propose a dimension-adaptive combination technique. In addition, a sparse grid quadrature for the high-dimensional parametric approximation is employed and simultaneously balanced with the spatial and stochastic approximation. Our adaptive algorithm constructs a sparse grid approximation based on the benefit-cost ratio such that the regularity and thus the decay of the Karhunen-Lo\`eve coefficients is not required beforehand. The decay is detected and exploited as the algorithm adjusts to the anisotropy in the parametric variables. We include numerical examples for the Darcy problem with a lognormal permeability field, which illustrate a good performance of the algorithm: For sufficiently smooth random fields, we essentially recover the spatial order of convergence as asymptotic convergence rate with respect to the computational cost.