We verify strong rates of convergence for a time-implicit, finite-element based space-time discretization of the backward stochastic heat equation, and the forward-backward stochastic heat equation from stochastic optimal control. The fully discrete version of the forward-backward stochastic heat equation is then used within a gradient descent algorithm to approximately solve the linear-quadratic control problem for the stochastic heat equation driven by additive noise. This work is thus giving a theoretical foundation for the computational findings in Dunst and Prohl, SIAM J. Sci. Comput. 38 (2016) A2725–A2755
Let X be the mild solution of a stochastic heat equation taking values in a Hilbert space H=L^2((0,1...
AbstractIn this paper, we establish the null/approximate controllability for forward stochastic heat...
This paper studies the convergence of a spatial semi-discretization for a backward semilinear stocha...
We verify strong rates of convergence for a time-implicit, finite-element based space-time discretiz...
The stochastic heat equation driven by additive noise is discretized in the spatial variables by a s...
We find the weak rate of convergence of approximate solutions of the nonlinear stochastic heat equat...
We consider a numerical approximation of a linear quadratic control problem constrained by the stoch...
This article establishes optimal upper and lower error estimates for strong full-discrete numerical ...
We present an abstract framework for analyzing the weak error of fully discrete approximation scheme...
This thesis addresses questions related to approximation arising from the fields of stochastic analys...
Abstract. We study algorithms for approximation of the mild solution of stochastic heat equations on...
Abstract. A unified approach is given for the analysis of the weak error of spatially semidiscrete f...
In this paper, we propose a deep learning based numerical scheme for strongly coupled forward backwa...
A fully discrete approximation of the one-dimensional stochastic heat equation driven by multiplicat...
This thesis is concerned with numerical approximation of linear stochastic partialdifferential equat...
Let X be the mild solution of a stochastic heat equation taking values in a Hilbert space H=L^2((0,1...
AbstractIn this paper, we establish the null/approximate controllability for forward stochastic heat...
This paper studies the convergence of a spatial semi-discretization for a backward semilinear stocha...
We verify strong rates of convergence for a time-implicit, finite-element based space-time discretiz...
The stochastic heat equation driven by additive noise is discretized in the spatial variables by a s...
We find the weak rate of convergence of approximate solutions of the nonlinear stochastic heat equat...
We consider a numerical approximation of a linear quadratic control problem constrained by the stoch...
This article establishes optimal upper and lower error estimates for strong full-discrete numerical ...
We present an abstract framework for analyzing the weak error of fully discrete approximation scheme...
This thesis addresses questions related to approximation arising from the fields of stochastic analys...
Abstract. We study algorithms for approximation of the mild solution of stochastic heat equations on...
Abstract. A unified approach is given for the analysis of the weak error of spatially semidiscrete f...
In this paper, we propose a deep learning based numerical scheme for strongly coupled forward backwa...
A fully discrete approximation of the one-dimensional stochastic heat equation driven by multiplicat...
This thesis is concerned with numerical approximation of linear stochastic partialdifferential equat...
Let X be the mild solution of a stochastic heat equation taking values in a Hilbert space H=L^2((0,1...
AbstractIn this paper, we establish the null/approximate controllability for forward stochastic heat...
This paper studies the convergence of a spatial semi-discretization for a backward semilinear stocha...