4 citations · 5 across the 7 of their papers we have counts for
7 papers
Deep Policy Iteration for High-Dimensional Mean-Field Games with Regenerative Reformulation
Shuixin Fang, Shupeng Wang, Zhen Wu +2
This paper develops a deep policy iteration method for high-dimensional finite-horizon mean-field games (MFG). We reformulate the game as a regenerative problem with deterministic…
A derivative-free localized stochastic method for very high-dimensional semilinear parabolic PDEs
Shuixin Fang, Changtao Sheng, Bihao Su +1
We develop a mesh-free, derivative-free, matrix-free, and highly parallel localized stochastic method for high-dimensional semilinear parabolic PDEs. The efficiency of the proposed…
Explicit Runge-Kutta schemes for Backward Stochastic Differential Equations
Shuixin Fang, Yue Qiu, Weidong Zhao
The Butcher theory provides a powerful tool for analyzing order conditions of Runge-Kutta schemes for ordinary differential equations (ODEs); however, such a theory has not yet bee…
Deep random difference method for high-dimensional quasilinear parabolic partial differential equations
Wei Cai, Shuixin Fang, Tao Zhou
Solving high-dimensional parabolic partial differential equations (PDEs) with deep learning methods is often computationally and memory intensive, primarily due to the need for aut…
Martingale deep neural network for very high-dimensional stochastic optimal controls
Wei Cai, Shuixin Fang, Wenzhong Zhang +1
We propose a martingale deep learning method for very high-dimensional stochastic optimal control problems (SOCPs) via their associated Hamilton--Jacobi--Bellman equations and the…
SOC-MartNet: A Martingale Neural Network for the Hamilton-Jacobi-Bellman Equation without Explicit inf H in Stochastic Optimal Controls
Wei Cai, Shuixin Fang, Tao Zhou
In this paper, we propose a martingale-based neural network, SOC-MartNet, for solving high-dimensional Hamilton-Jacobi-Bellman (HJB) equations where no explicit expression is neede…