A Bramble-Pasciak conjugate gradient method for discrete Stokes problems with lognormal random viscosity

TL;DR

This paper introduces a novel Bramble-Pasciak conjugate gradient method with a block triangular preconditioner for solving high-dimensional stochastic saddle point problems, demonstrating improved convergence over standard methods in a stochastic Stokes flow model.

Contribution

The paper develops and analyzes a new Bramble-Pasciak conjugate gradient method tailored for stochastic saddle point systems with random viscosity, offering better approximation and convergence properties.

Findings

Bramble-Pasciak method outperforms MINRES in iteration counts

Eigenvalue estimates support convergence analysis

Numerical tests confirm improved efficiency in stochastic Stokes problems

Abstract

We study linear systems of equations arising from a stochastic Galerkin finite element discretization of saddle point problems with random data and its iterative solution. We consider the Stokes flow model with random viscosity described by the exponential of a correlated random process and shortly discuss the discretization framework and the representation of the emerging matrix equation. Due to the high dimensionality and the coupling of the associated symmetric, indefinite, linear system, we resort to iterative solvers and problem-specific preconditioners. As a standard iterative solver for this problem class, we consider the block diagonal preconditioned MINRES method and further introduce the Bramble-Pasciak conjugate gradient method as a promising alternative. This special conjugate gradient method is formulated in a non-standard inner product with a block triangular preconditioner. From a structural point of view, such a block triangular preconditioner enables a better approximation of the original problem than the block diagonal one. We derive eigenvalue estimates to assess the convergence behavior of the two solvers with respect to relevant physical and numerical parameters and verify our findings by the help of a numerical test case. We model Stokes flow in a cavity driven by a moving lid and describe the viscosity by the exponential of a truncated Karhunen-Loeve expansion. Regarding iteration counts, the Bramble-Pasciak conjugate gradient method with block triangular preconditioner is superior to the MINRES method with block diagonal preconditioner in the considered example.