no code implementations • 12 Apr 2022 • Shaolin Ji, Shige Peng, Ying Peng, Xichuan Zhang
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 equations (FBSDEs in short) through deep learning.
no code implementations • 4 Nov 2021 • Shaolin Ji, Shige Peng, Ying Peng, Xichuan Zhang
In this paper, we mainly focus on solving high-dimensional stochastic Hamiltonian systems with boundary condition, which is essentially a Forward Backward Stochastic Differential Equation (FBSDE in short), and propose a novel method from the view of the stochastic control.
1 code implementation • 5 Jul 2020 • Shaolin Ji, Shige Peng, Ying Peng, Xichuan Zhang
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.
no code implementations • 11 Jul 2019 • Shaolin Ji, Shige Peng, Ying Peng, Xichuan Zhang
Recently, the deep learning method has been used for solving forward-backward stochastic differential equations (FBSDEs) and parabolic partial differential equations (PDEs).
