We propose an efficient and robust multiscale preconditioner for large-scale incompressible
flow in highly heterogeneous porous media. We start from the discretization of the first-order …

We here propose a multiscale numerical method for the solution of stochastic parametric
partial differential equations with localized uncertainties described with a finite number of …

In this paper, we study two-grid preconditioners for three-dimensional single-phase
nonlinear compressible flow in highly heterogeneous porous media arising from reservoir …

A multiscale numerical method is proposed for the solution of semi-linear elliptic stochastic
partial differential equations with localized uncertainties and non-linearities, the …

We present an efficient approach for preconditioning systems arising in multiphase flow in a
parallel domain decomposition framework known as the mortar mixed finite element method …

The aim of this paper is to introduce a new approach to efficiently solve sequences of
problems that typically arise when modeling flow in stochastic porous media. The governing …

In this paper we present a stochastic dimension reduction multiscale finite element method
for solving groundwater flow problems in heterogeneous random porous media. The …

Three algorithms are developed for uncertainty quantification in modeling coupled Stokes
and Darcy flows. The porous media may consist of multiple regions with different properties …

In this paper, we propose a deep learning based reduced order modeling method for
stochastic underground flow problems in highly heterogeneous media. We aim to utilize …

Stochastic multiscale modeling has become a necessary approach to quantify uncertainty
and characterize multiscale phenomena for many practical problems such as flows in …