In this work, we consider an elliptic partial differential equation (PDE) with a random coefficient solved with the stochastic collocation finite element method (SC-FEM). The random diffusion coefficient is assumed to depend in an affine way on independent random variables. We derive a residual-based a posteriori error estimate that is constituted of two parts controlling the SC error and the FE error, respectively. The SC error estimator is then used to drive an adaptive sparse grid algorithm. Several numerical examples are given to illustrate the efficiency of the error estimator and the performance of the adaptive algorithm
Abstract. Partial differential equations (PDEs) are widely used for modelling problems in many f...
The perturbation method has been among the most popular stochastic finite element methods due to its...
Stochastic spectral methods are numerical techniques for approximating solutions to partial differen...
In this work, we consider an elliptic partial differential equation with a random coefficient solved...
In this work we present a residual based a posteriori error estimation for a heat equation with a ra...
This thesis is devoted to the derivation of error estimates for partial differential equations with ...
In this paper, a finite element error analysis is performed on a class of linear and nonlinear ellip...
The stochastic collocation method based on the anisotropic sparse grid has become a significant tool...
Convergence of an adaptive collocation method for the stationary parametric diffusion equation with ...
We consider an elliptic partial differential equation with a random diffusion parameter discretized ...
Equilibration error estimators have been shown to commonly lead to very accurate guaranteed error bo...
The paper considers a class of parametric elliptic partial differential equations (PDEs), where the ...
A numerical method for the fully adaptive sampling and interpolation of PDE with random data is pres...
A numerical method for the fully adaptive sampling and interpolation of PDE with random data is pres...
We analyze a-posteriori error estimation and adaptive refinement algorithms for stochastic Galerkin ...
Abstract. Partial differential equations (PDEs) are widely used for modelling problems in many f...
The perturbation method has been among the most popular stochastic finite element methods due to its...
Stochastic spectral methods are numerical techniques for approximating solutions to partial differen...
In this work, we consider an elliptic partial differential equation with a random coefficient solved...
In this work we present a residual based a posteriori error estimation for a heat equation with a ra...
This thesis is devoted to the derivation of error estimates for partial differential equations with ...
In this paper, a finite element error analysis is performed on a class of linear and nonlinear ellip...
The stochastic collocation method based on the anisotropic sparse grid has become a significant tool...
Convergence of an adaptive collocation method for the stationary parametric diffusion equation with ...
We consider an elliptic partial differential equation with a random diffusion parameter discretized ...
Equilibration error estimators have been shown to commonly lead to very accurate guaranteed error bo...
The paper considers a class of parametric elliptic partial differential equations (PDEs), where the ...
A numerical method for the fully adaptive sampling and interpolation of PDE with random data is pres...
A numerical method for the fully adaptive sampling and interpolation of PDE with random data is pres...
We analyze a-posteriori error estimation and adaptive refinement algorithms for stochastic Galerkin ...
Abstract. Partial differential equations (PDEs) are widely used for modelling problems in many f...
The perturbation method has been among the most popular stochastic finite element methods due to its...
Stochastic spectral methods are numerical techniques for approximating solutions to partial differen...