Stochastic Galerkin finite element methods (SGFEMs) are commonly used to approximate solutions to PDEs with random inputs. However, the study of a posteriori error estimation strategies to drive adaptive enrichment of the associated tensor product spaces is still in its infancy. In this work, we revisit an error estimator introduced in [A. Bespalov, D. Silvester, Efficient adaptive stochastic Galerkin methods for parametric operator equations, SIAM J. Sci. Comput., 38(4), 2016] for SGFEM approximations of the parametric reformulation of the stochastic diffusion problem. We show that the proven bound for that error estimator can be derived using classical theory that is well known for determinstic Galerkin FEMs. A key issue is that the bound...
We construct stochastic Galerkin approximations to the solution of a first order system of PDEs with...
In this work, we consider an elliptic partial differential equation (PDE) with a random coefficient ...
The solution of PDE with stochastic data commonly leads to very high-dimensional algebraic problems,...
summary:We introduce a new tool for obtaining efficient a posteriori estimates of errors of approxim...
We analyze a-posteriori error estimation and adaptive refinement algorithms for stochastic Galerkin ...
A framework for residual-based a posteriori error estimation and adaptive mesh refinement and polyno...
Stochastic Galerkin approximation is an increasingly popular approach for the solution of elliptic P...
This paper is concerned with the design and implementation of efficient solution algorithms for ell...
Equilibration error estimators have been shown to commonly lead to very accurate guaranteed error bo...
The solution of PDE with stochastic data commonly leads to very high-dimensional algebraic problems,...
Numerical methods for random parametric PDEs can greatly benefit from adaptive refinement schemes, i...
Equilibration error estimators have been shown to commonly lead to very accurate guaranteed error bo...
In this thesis, we focus on the design of efficient adaptive algorithms for the numerical approximat...
Dedicated to Professor Norbert Heuer on the occasion of his 50th birthday Abstract. Stochastic Galer...
The paper considers a class of parametric elliptic partial differential equations (PDEs), where the ...
We construct stochastic Galerkin approximations to the solution of a first order system of PDEs with...
In this work, we consider an elliptic partial differential equation (PDE) with a random coefficient ...
The solution of PDE with stochastic data commonly leads to very high-dimensional algebraic problems,...
summary:We introduce a new tool for obtaining efficient a posteriori estimates of errors of approxim...
We analyze a-posteriori error estimation and adaptive refinement algorithms for stochastic Galerkin ...
A framework for residual-based a posteriori error estimation and adaptive mesh refinement and polyno...
Stochastic Galerkin approximation is an increasingly popular approach for the solution of elliptic P...
This paper is concerned with the design and implementation of efficient solution algorithms for ell...
Equilibration error estimators have been shown to commonly lead to very accurate guaranteed error bo...
The solution of PDE with stochastic data commonly leads to very high-dimensional algebraic problems,...
Numerical methods for random parametric PDEs can greatly benefit from adaptive refinement schemes, i...
Equilibration error estimators have been shown to commonly lead to very accurate guaranteed error bo...
In this thesis, we focus on the design of efficient adaptive algorithms for the numerical approximat...
Dedicated to Professor Norbert Heuer on the occasion of his 50th birthday Abstract. Stochastic Galer...
The paper considers a class of parametric elliptic partial differential equations (PDEs), where the ...
We construct stochastic Galerkin approximations to the solution of a first order system of PDEs with...
In this work, we consider an elliptic partial differential equation (PDE) with a random coefficient ...
The solution of PDE with stochastic data commonly leads to very high-dimensional algebraic problems,...