Add to ORKGIn the last five years, the scientific computing community has taken great interest in the so-called stochastic Galerkin schemes (SGS). These schemes are typically used to address the following type of problem: Suppose that I have a deterministic differential equation model for which I can find an approximate solution using standard numerical discretization techniques. Now suppose that, in an attempt to improve the model,
Introduction For the last thirty years, there has been interest in numerical simulation of solution...
Abstract. Over the last few years there have been dramatic advances in our understanding of mathemat...
We introduce a general framework for approximating parabolic Stochastic Partial Differential Equatio...
A stochastic differential equation is a differential equation which contains at least one stochastic...
The stochastic finite element method is a recent technique for solving partial differential equation...
The stochastic Galerkin finite element method provides a powerful tool for computing high-order stoc...
The stochastic collocation and Galerkin method are two state-of-the-art tools for solving stochastic...
It is common practice in the study of stochastic Galerkin methods for boundary value problems depend...
Mathematical models of engineering systems and physical processes typically take the form of a parti...
Mathematical models of engineering systems and physical processes typically take the form of a parti...
Linear dynamical systems are considered in form of ordinary differential equations or differential a...
This thesis explains the theoretical background of stochastic differential equations in one dimensio...
2013-08-02This dissertation focuses on facilitating the analysis of probabilistic models for physica...
We introduce a general framework for approximating parabolic Stochastic Partial Differential Equatio...
The goal of this paper is to present a series of recent contributions arising in numerical probabili...
Introduction For the last thirty years, there has been interest in numerical simulation of solution...
Abstract. Over the last few years there have been dramatic advances in our understanding of mathemat...
We introduce a general framework for approximating parabolic Stochastic Partial Differential Equatio...
A stochastic differential equation is a differential equation which contains at least one stochastic...
The stochastic finite element method is a recent technique for solving partial differential equation...
The stochastic Galerkin finite element method provides a powerful tool for computing high-order stoc...
The stochastic collocation and Galerkin method are two state-of-the-art tools for solving stochastic...
It is common practice in the study of stochastic Galerkin methods for boundary value problems depend...
Mathematical models of engineering systems and physical processes typically take the form of a parti...
Mathematical models of engineering systems and physical processes typically take the form of a parti...
Linear dynamical systems are considered in form of ordinary differential equations or differential a...
This thesis explains the theoretical background of stochastic differential equations in one dimensio...
2013-08-02This dissertation focuses on facilitating the analysis of probabilistic models for physica...
We introduce a general framework for approximating parabolic Stochastic Partial Differential Equatio...
The goal of this paper is to present a series of recent contributions arising in numerical probabili...
Introduction For the last thirty years, there has been interest in numerical simulation of solution...
Abstract. Over the last few years there have been dramatic advances in our understanding of mathemat...
We introduce a general framework for approximating parabolic Stochastic Partial Differential Equatio...