37,350 research outputs found
SOLUTION STRATEGIES FOR STOCHASTIC FINITE ELEMENT DISCRETIZATIONS
We consider efficient numerical methods for the solution of partial differential equations with stochastic coefficients or right hand side. The discretization is performed by the stochastic finite element method (SFEM). Separation of spatial and stochastic variables in the random input data is achieved via a Karhunen-Loève expansion or Wiener's polynomial chaos expansion. We discuss solution strategies for the Galerkin system that take advantage of the special structure of the system matrix. For stochastic coefficients linear in a set of independent random variables we employ Krylov subspace recycling techniques after having decoupled the large SFEM stiffness matrix
Factorizing the Stochastic Galerkin System
Recent work has explored solver strategies for the linear system of equations
arising from a spectral Galerkin approximation of the solution of PDEs with
parameterized (or stochastic) inputs. We consider the related problem of a
matrix equation whose matrix and right hand side depend on a set of parameters
(e.g. a PDE with stochastic inputs semidiscretized in space) and examine the
linear system arising from a similar Galerkin approximation of the solution. We
derive a useful factorization of this system of equations, which yields bounds
on the eigenvalues, clues to preconditioning, and a flexible implementation
method for a wide array of problems. We complement this analysis with (i) a
numerical study of preconditioners on a standard elliptic PDE test problem and
(ii) a fluids application using existing CFD codes; the MATLAB codes used in
the numerical studies are available online.Comment: 13 pages, 4 figures, 2 table
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