5 papers
An efficient solver based on low-rank approximation and Neumann matrix series for unsteady diffusion-type partial differential equations with random coefficients
Yujun Zhu, Min Li, Yulan Ning +1
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…
A low-rank solver for the Stokes-Darcy model with random hydraulic conductivity and Beavers-Joseph condition
Yujun Zhu, Yulan Ning, Zhipeng Yang +2
This paper proposes, analyzes, and demonstrates an efficient low-rank solver for the stochastic Stokes-Darcy interface model with a random hydraulic conductivity both in the porous…
Splitting Method for Stochastic Navier-Stokes Equations
Jie Zhu, Yujun Zhu, Ju Ming +1
This paper investigates the two-dimensional stochastic steady-state Navier-Stokes(NS) equations with additive random noise. We introduce an innovative splitting method that decompo…
Clustering-based Low Rank Approximation Method
Yujun Zhu, Jie Zhu, Hizba Arshad +2
We propose a clustering-based generalized low rank approximation method, which takes advantage of appealing features from both the generalized low rank approximation of matrices (G…
Low rank approximation method for perturbed linear systems with applications to elliptic type stochastic PDEs
Yujun Zhu, Ju Ming, Jie Zhu +1
In this paper, we propose a low rank approximation method for efficiently solving stochastic partial differential equations. Specifically, our method utilizes a novel low rank appr…