Stochastic Galerkin finite element approximation of PDEs with random inputs leads to linear
systems of equations with coefficient matrices that have a characteristic Kronecker product …

We study the steady-state Navier–Stokes equations in the context of stochastic finite element
discretizations. Specifically, we assume that the viscosity is a random field given in the form …

Stochastic Galerkin finite element discretizations of partial differential equations with
coefficients characterized by arbitrary distributions lead, in general, to fully block dense …

We study an iterative low-rank approximation method for the solution of the steady-state
stochastic Navier--Stokes equations with uncertain viscosity. The method is based on …

In this paper we introduce and analyze the Schur complement technique to a weak Galerkin
(WG for short) finite element method for solving Stokes equation. Due to the special structure …

Stochastic finite element method (StFEM) is a robust tool for uncertainty quantification of
engineering systems having random properties. Nevertheless, the matrices involved in this …

We present a reduced basis stochastic Galerkin method for partial differential equations with
random inputs. In this method, the reduced basis methodology is integrated into the …

In this study, a robust and effective preconditioner for the fast method of moments‐based
hierarchal electric field integral equation solver is proposed using symmetric near‐field …

The computational cost associated with uncertainty quantification of engineering problems
featuring localized phenomenon can be reduced by confining the random variability of the …

The stochastic Galerkin finite element method (SGFEM) provides an efficient alternative to
traditional sampling methods for the numerical solution of linear elliptic partial differential …