Stochastic basis adaptation and spatial domain decomposition for PDEs with random coefficients

TL;DR

This paper introduces a domain decomposition and stochastic basis adaptation method for high-dimensional stochastic PDEs, significantly reducing computational costs and enabling parallel computation while maintaining accuracy.

Contribution

The paper proposes a novel combined approach of domain decomposition and local stochastic basis adaptation to efficiently solve high-dimensional stochastic PDEs with reduced computational complexity.

Findings

Reduces operation count from $O(N^\alpha)$ to $O(M^\alpha)$

Highly parallelizable due to localized solutions

Accurate results for linear and nonlinear stochastic PDEs

Abstract

We present a novel uncertainty quantification approach for high-dimensional stochastic partial differential equations that reduces the computational cost of polynomial chaos methods by decomposing the computational domain into non-overlapping subdomains and adapting the stochastic basis in each subdomain so the local solution has a lower dimensional random space representation. The local solutions are coupled using the Neumann-Neumann algorithm, where we first estimate the interface solution then evaluate the interior solution in each subdomain using the interface solution as a boundary condition. The interior solutions in each subdomain are computed independently of each other, which reduces the operation count from $O(N^\alpha)$ to $O(M^\alpha),$ where $N$ is the total number of degrees of freedom, $M$ is the number of degrees of freedom in each subdomain, and the exponent $\alpha>1$ depends on the uncertainty quantification method used. In addition, the localized nature of solutions makes the proposed approach highly parallelizable. We illustrate the accuracy and efficiency of the approach for linear and nonlinear differential equations with random coefficients.