We derive an analytical weak approximation of a multidimensional diffusion process as coefficients or time are small. Our methodology combines the use of Gaussian proxys to approximate the law of the diffusion and a Finite Element interpolation of the terminal function applied to the diffusion. We call this method Stochastic Approximation Finite Element (SAFE for short) method. We provide error bounds of our global approximation depending on the diffusion process coefficients, the time horizon and the regularity of the terminal function. Then we give estimates of the computational cost of our algorithm. This shows an improved efficiency compared to Monte-Carlo methods in small and medium dimensions (up to 10), which is confirmed by numerica...
We present an abstract framework for analyzing the weak error of fully discrete approximation scheme...
Numerical methods for stochastic differential equations, including Taylor expansion approximations, ...
AbstractThe Euler scheme is a well-known method of approximation of solutions of stochastic differen...
We derive an analytical weak approximation of a multidimensional diffusion process as coefficients o...
Stochastic partial differential equations (SPDE) must be approximated in space and time to allow for...
The thesis consists of four papers on numerical complexityanalysis of weak approximation of ordinary...
International audienceWe describe Monte Carlo algorithms to solve elliptic partial differen- tial eq...
Numerical methods for solving the diffusion equation are based on discretizing space and time so as ...
This thesis deals with the approximation of the expectation of a functional (possibly depending on t...
In order to simulate solutions to stochastic partial differential equations (SPDE) they must be appr...
The stochastic finite element method is an important technique for solving stochastic partial differ...
The discretization of the stationary diffusion equation with random parameters by the Stochastic Fin...
It is the purpose of this thesis to develop iterative methods for solving the linear systems that ar...
In a number of problems of mathematical physics and other fields stochastic differential equations a...
The stochastic finite element method is a recent technique for solving partial differential equation...
We present an abstract framework for analyzing the weak error of fully discrete approximation scheme...
Numerical methods for stochastic differential equations, including Taylor expansion approximations, ...
AbstractThe Euler scheme is a well-known method of approximation of solutions of stochastic differen...
We derive an analytical weak approximation of a multidimensional diffusion process as coefficients o...
Stochastic partial differential equations (SPDE) must be approximated in space and time to allow for...
The thesis consists of four papers on numerical complexityanalysis of weak approximation of ordinary...
International audienceWe describe Monte Carlo algorithms to solve elliptic partial differen- tial eq...
Numerical methods for solving the diffusion equation are based on discretizing space and time so as ...
This thesis deals with the approximation of the expectation of a functional (possibly depending on t...
In order to simulate solutions to stochastic partial differential equations (SPDE) they must be appr...
The stochastic finite element method is an important technique for solving stochastic partial differ...
The discretization of the stationary diffusion equation with random parameters by the Stochastic Fin...
It is the purpose of this thesis to develop iterative methods for solving the linear systems that ar...
In a number of problems of mathematical physics and other fields stochastic differential equations a...
The stochastic finite element method is a recent technique for solving partial differential equation...
We present an abstract framework for analyzing the weak error of fully discrete approximation scheme...
Numerical methods for stochastic differential equations, including Taylor expansion approximations, ...
AbstractThe Euler scheme is a well-known method of approximation of solutions of stochastic differen...