1 citations · 1 across the 3 of their papers we have counts for
5 papers
A Posteriori Error Analysis for Decoupled Neural Approximations of Fully Coupled FBSDEs with Control Mismatch
Xichuan Zhang
This paper develops an a posteriori error analysis framework for decoupled neural approximations of fully coupled forward--backward stochastic differential equations (FBSDEs). It p…
A deep learning method for solving stochastic optimal control problems driven by fully-coupled FBSDEs
Shaolin Ji, Shige Peng, Ying Peng +1
In this paper,we mainly focus on the numerical solution of high-dimensional stochastic optimal control problem driven by fully-coupled forward-backward stochastic differential equa…
A novel control method for solving high-dimensional Hamiltonian systems through deep neural networks
Shaolin Ji, Shige Peng, Ying Peng +1
In this paper, we mainly focus on solving high-dimensional stochastic Hamiltonian systems with boundary condition, which is essentially a Forward Backward Stochastic Differential E…
Solving stochastic optimal control problem via stochastic maximum principle with deep learning method
Shaolin Ji, Shige Peng, Ying Peng +1
In this paper, we aim to solve the high dimensional stochastic optimal control problem from the view of the stochastic maximum principle via deep learning. By introducing the exten…
Three algorithms for solving high-dimensional fully-coupled FBSDEs through deep learning
Shaolin Ji, Shige Peng, Ying Peng +1
Recently, the deep learning method has been used for solving forward-backward stochastic differential equations (FBSDEs) and parabolic partial differential equations (PDEs). It has…