Cluster-based Generalized Multiscale Finite Element Method for elliptic PDEs with random coefficients

TL;DR

This paper introduces a clustering-based generalized multiscale finite element method (GMsFEM) for efficiently solving elliptic PDEs with random coefficients, especially in multi-query stochastic settings, reducing computational costs while maintaining accuracy.

Contribution

The paper develops a novel GMsFEM framework that combines clustering in the random space with multiscale basis construction, enabling efficient solutions for multiscale stochastic PDEs without requiring fine grids.

Findings

Significant computational savings demonstrated in numerical experiments.

High accuracy maintained even with coarse initial grids.

Effective handling of multiscale stochastic problems without scale separation.

Abstract

We propose a generalized multiscale finite element method (GMsFEM) based on clustering algorithm to study the elliptic PDEs with random coefficients in the multi-query setting. Our method consists of offline and online stages. In the offline stage, we construct a small number of reduced basis functions within each coarse grid block, which can then be used to approximate the multiscale finite element basis functions. In addition, we coarsen the corresponding random space through a clustering algorithm. In the online stage, we can obtain the multiscale finite element basis very efficiently on a coarse grid by using the pre-computed multiscale basis. The new GMsFEM can be applied to multiscale SPDE starting with a relatively coarse grid, without requiring the coarsest grid to resolve the smallest-scale of the solution. The new method offers considerable savings in solving multiscale SPDEs. Numerical results are presented to demonstrate the accuracy and efficiency of the proposed method for several multiscale stochastic problems without scale separation.