Add to ORKGOtto-von-Guericke-Universität Magdeburg, Fakultät für Mathematik, Dissertation, 2016von Dr. rer. pol. Akwum Agwu OnwuntaLiteraturverzeichnis: Seite 135-14
We consider the numerical solution of a steady-state diffusion problem where the diffusion coeffici...
Stochastic Galerkin methods for non-affine coefficient representations are known to cause major diff...
The discretization of the stationary diffusion equation with random parameters by the Stochastic Fin...
Stochastic Galerkin finite element approximation of PDEs with random inputs leads to linear systems ...
The solution of PDE with stochastic data commonly leads to very high-dimensional algebraic problems,...
The solution of PDE with stochastic data commonly leads to very high-dimensional algebraic problems,...
Stochastic partial differential equations are widely used to model physical problems with uncertaint...
2013-08-02This dissertation focuses on facilitating the analysis of probabilistic models for physica...
A linear PDE problem for randomly perturbed domains is considered in an adaptive Galerkin framework....
The stochastic finite element method is a recent technique for solving partial differential equation...
Spektrale stochastische Methoden haben sich als effizientes Werkzeug zur Modellierung von Systemen m...
In this paper, we introduce and analyze a new low-rank multilevel strategy for the solution of rando...
This dissertation presents efficient and optimal numerical algorithms for the solution of parameteri...
AbstractFor parametrised equations, which arise, for example, in equations dependent on random param...
Partial differential equations (PDEs) with random input data, such as random loadings and coefficien...
We consider the numerical solution of a steady-state diffusion problem where the diffusion coeffici...
Stochastic Galerkin methods for non-affine coefficient representations are known to cause major diff...
The discretization of the stationary diffusion equation with random parameters by the Stochastic Fin...
Stochastic Galerkin finite element approximation of PDEs with random inputs leads to linear systems ...
The solution of PDE with stochastic data commonly leads to very high-dimensional algebraic problems,...
The solution of PDE with stochastic data commonly leads to very high-dimensional algebraic problems,...
Stochastic partial differential equations are widely used to model physical problems with uncertaint...
2013-08-02This dissertation focuses on facilitating the analysis of probabilistic models for physica...
A linear PDE problem for randomly perturbed domains is considered in an adaptive Galerkin framework....
The stochastic finite element method is a recent technique for solving partial differential equation...
Spektrale stochastische Methoden haben sich als effizientes Werkzeug zur Modellierung von Systemen m...
In this paper, we introduce and analyze a new low-rank multilevel strategy for the solution of rando...
This dissertation presents efficient and optimal numerical algorithms for the solution of parameteri...
AbstractFor parametrised equations, which arise, for example, in equations dependent on random param...
Partial differential equations (PDEs) with random input data, such as random loadings and coefficien...
We consider the numerical solution of a steady-state diffusion problem where the diffusion coeffici...
Stochastic Galerkin methods for non-affine coefficient representations are known to cause major diff...
The discretization of the stationary diffusion equation with random parameters by the Stochastic Fin...