Exploring the Locally Low Dimensional Structure in Solving Random Elliptic PDEs

Thomas Y. Hou, Qin Li, Pengchuan Zhang
2017 Multiscale Modeling & simulation  
We propose a stochastic multiscale finite element method (StoMsFEM) to solve random elliptic partial differential equations with a high stochastic dimension. The key idea is to simultaneously upscale the stochastic solutions in the physical space for all random samples and explore the low stochastic dimensions of the stochastic solution within each local patch. We propose two effective methods to achieve this simultaneous local upscaling. The first method is a high order interpolation method in
more » ... rpolation method in the stochastic space that explores the high regularity of the local upscaled quantities with respect to the random variables. The second method is a reduced-order method that explores the low rank property of the multiscale basis functions within each coarse grid patch. Our complexity analysis shows that compared with the standard FEM on a fine grid, the StoMsFEM can achieve computational saving in the order of (H/h) d /(log(H/h)) k , where H/h is the ratio between the coarse and the fine gird sizes, d is the physical dimension and k is the local stochastic dimension. Several numerical examples are presented to demonstrate the accuracy and effectiveness of the proposed methods. In the high contrast example, we observe a factor of 2000 speed-up. Date: December 7, 2016. Applied and Comput. Math,
doi:10.1137/16m1077611 fatcat:zng4kpxwmrd25nftir6r55vqne