open3siStochastic Galerkin finite element approximation of PDEs with random inputs leads to linear systems of equations with coefficient matrices that have a characteristic Kronecker product structure. By reformulating the systems as multiterm linear matrix equations, we develop an efficient solution algorithm which generalizes ideas from rational Krylov subspace approximation. Our working assumptions are that the number of random variables characterizing the random inputs is modest, in the order of a few tens, and that the dependence on these variables is linear, so that it is sufficient to seek only a reduction in the complexity associated with the spatial component of the approximation space. The new approach determines a low-rank approx...
This paper discusses the design and implementation of efficient solution algorithms for symmetric li...
summary:We introduce a new tool for obtaining efficient a posteriori estimates of errors of approxim...
Abstract. In this work we first focus on the Stochastic Galerkin approximation of the solution u of ...
Stochastic Galerkin finite element approximation of PDEs with random inputs leads to linear systems ...
Stochastic Galerkin finite element approximation of PDEs with random inputs leads to linear systems ...
2013-08-02This dissertation focuses on facilitating the analysis of probabilistic models for physica...
We consider efficient numerical methods for the solution of partial differential equations with stoc...
Otto-von-Guericke-Universität Magdeburg, Fakultät für Mathematik, Dissertation, 2016von Dr. rer. pol...
In this work we first focus on the Stochastic Galerkin approximation of the solution $u$ of an ellip...
We consider the numerical solution of a steady-state diffusion problem where the diffusion coeffici...
The discretization of the stationary diffusion equation with random parameters by the Stochastic Fin...
Abstract. Mixed finite element discretizations of deterministic second-order elliptic partial differ...
Abstract. Mixed finite element discretizations of deterministic second-order elliptic partial differ...
The solution of PDE with stochastic data commonly leads to very high-dimensional algebraic problems,...
summary:We examine different approaches to an efficient solution of the stochastic Galerkin (SG) mat...
This paper discusses the design and implementation of efficient solution algorithms for symmetric li...
summary:We introduce a new tool for obtaining efficient a posteriori estimates of errors of approxim...
Abstract. In this work we first focus on the Stochastic Galerkin approximation of the solution u of ...
Stochastic Galerkin finite element approximation of PDEs with random inputs leads to linear systems ...
Stochastic Galerkin finite element approximation of PDEs with random inputs leads to linear systems ...
2013-08-02This dissertation focuses on facilitating the analysis of probabilistic models for physica...
We consider efficient numerical methods for the solution of partial differential equations with stoc...
Otto-von-Guericke-Universität Magdeburg, Fakultät für Mathematik, Dissertation, 2016von Dr. rer. pol...
In this work we first focus on the Stochastic Galerkin approximation of the solution $u$ of an ellip...
We consider the numerical solution of a steady-state diffusion problem where the diffusion coeffici...
The discretization of the stationary diffusion equation with random parameters by the Stochastic Fin...
Abstract. Mixed finite element discretizations of deterministic second-order elliptic partial differ...
Abstract. Mixed finite element discretizations of deterministic second-order elliptic partial differ...
The solution of PDE with stochastic data commonly leads to very high-dimensional algebraic problems,...
summary:We examine different approaches to an efficient solution of the stochastic Galerkin (SG) mat...
This paper discusses the design and implementation of efficient solution algorithms for symmetric li...
summary:We introduce a new tool for obtaining efficient a posteriori estimates of errors of approxim...
Abstract. In this work we first focus on the Stochastic Galerkin approximation of the solution u of ...