An inexact Newton-Krylov method for stochastic eigenvalue problems

Abstract

This paper aims at the efficient numerical solution of stochastic eigenvalue problems. Such problems often lead to prohibitively high dimensional systems with tensor product structure when discretized with the stochastic Galerkin method. Here, we exploit this inherent tensor product structure to develop a globalized low-rank inexact Newton method with which we tackle the stochastic eigenproblem. We illustrate the effectiveness of our solver with numerical experiments.

References in corpus (2)

• Composing Scalable Nonlinear Algebraic Solvers
• Inverse subspace iteration for spectral stochastic finite element methods

Cited by in corpus (4)

• On uncertainty quantification of eigenvalues and eigenspaces with higher multiplicity
• Prospects of tensor-based numerical modeling of the collective electrostatic potential in many-particle systems
• On Surrogate Learning for Linear Stability Assessment of Navier-Stokes Equations with Stochastic Viscosity
• Low-Rank Solution Methods for Stochastic Eigenvalue Problems