An efficient solver based on low-rank approximation and Neumann matrix series for unsteady diffusion-type partial differential equations with random coefficients

TL;DR

This paper introduces an efficient numerical solver for unsteady diffusion PDEs with random coefficients, utilizing low-rank approximations and Neumann series to reduce computational costs while maintaining accuracy.

Contribution

The paper presents a novel low-rank matrix approximation combined with Neumann series expansion to efficiently solve large-scale stochastic PDEs, reducing computational complexity.

Findings

Significant reduction in computational cost and storage.

High numerical accuracy maintained with the new approach.

Effective application to uncertainty quantification problems.

Abstract

In this paper, we develop an efficient numerical solver for unsteady diffusion-type partial differential equations with random coefficients. A major computational challenge in such problems lies in repeatedly handling large-scale linear systems arising from spatial and temporal discretizations under uncertainty. To address this issue, we propose a novel generalized low-rank matrix approximation to represent the stochastic stiffness matrices, and approximate their inverses using the Neumann matrix series expansion. This approach transforms high-dimensional matrix inversion into a sequence of low-dimensional matrix multiplications. Therefore, the solver significantly reduces the computational cost and storage requirements while maintaining high numerical accuracy. The error analysis of the proposed solver is also provided. Finally, we apply the method to two classic uncertainty quantification problems: unsteady stochastic diffusion equations and the associated distributed optimal control problems. Numerical results demonstrate the feasibility and effectiveness of the proposed solver.