This paper presents mutli-element Stochastic Reduced Basis Methods (ME-SRBMs) for solving linear stochastic partial differential equations. In ME-SRBMs, the domain of definition of the random inputs is decomposed into smaller subdomains or random elements. Stochastic Reduced Basis Methods (SRBMs) are employed in each random element to evaluated the response statistics. These elemental statistics are assimilated to compute the overall statistics. The effectiveness of the method is demonstrated by solving the stochastic steady state heat transfer equation on two geometries involving different types of boundary conditions. Numerical studies are conducted to investigate the h-convergence rates of global and local preconditioning strategies
We present a comparison of subspace projection schemes for stochastic finite element analysis in ter...
We consider the estimation of parameter-dependent statistics of functional outputs of elliptic bound...
In this work, a Reduced Basis (RB) approach is used to solve a large number of Boundary Value Proble...
The focus of the present work is to develop stochastic reduced basis methods (SRBMs) for solving par...
International audienceWe report here on the recent application of a now classical general reduction ...
The focus of this paper is to develop efficient numerical schemes for analysis of systems governed b...
In a companion paper (Nair, P.B., and Keane, A.J., 'New Developments in Computational Stochastic Mec...
Stochastic reduced basis methods for solving large-scale linear random algebraic systems of equation...
The stochastic finite element method is an important technique for solving stochastic partial differ...
2013-08-02This dissertation focuses on facilitating the analysis of probabilistic models for physica...
This research is concerned with the development of subspace projection schemes for efficiently solvi...
The stochastic finite element method is a recent technique for solving partial differential equation...
Stochastic reduced basis methods (SRBMs) are a class of numerical techniques for approximately compu...
This paper introduces stochastic reduced basis methods for solving largescale linear random algebra...
This thesis is concerned with the development of reduced basis methods for parametrized partial diff...
We present a comparison of subspace projection schemes for stochastic finite element analysis in ter...
We consider the estimation of parameter-dependent statistics of functional outputs of elliptic bound...
In this work, a Reduced Basis (RB) approach is used to solve a large number of Boundary Value Proble...
The focus of the present work is to develop stochastic reduced basis methods (SRBMs) for solving par...
International audienceWe report here on the recent application of a now classical general reduction ...
The focus of this paper is to develop efficient numerical schemes for analysis of systems governed b...
In a companion paper (Nair, P.B., and Keane, A.J., 'New Developments in Computational Stochastic Mec...
Stochastic reduced basis methods for solving large-scale linear random algebraic systems of equation...
The stochastic finite element method is an important technique for solving stochastic partial differ...
2013-08-02This dissertation focuses on facilitating the analysis of probabilistic models for physica...
This research is concerned with the development of subspace projection schemes for efficiently solvi...
The stochastic finite element method is a recent technique for solving partial differential equation...
Stochastic reduced basis methods (SRBMs) are a class of numerical techniques for approximately compu...
This paper introduces stochastic reduced basis methods for solving largescale linear random algebra...
This thesis is concerned with the development of reduced basis methods for parametrized partial diff...
We present a comparison of subspace projection schemes for stochastic finite element analysis in ter...
We consider the estimation of parameter-dependent statistics of functional outputs of elliptic bound...
In this work, a Reduced Basis (RB) approach is used to solve a large number of Boundary Value Proble...