Three Algorithms for Solving High-Dimensional Fully Coupled FBSDEs Through Deep Learning

Recently, the deep learning method has been used for solving forward–backward stochastic differential equations (FBSDEs) and parabolic partial differential equations, as it has good accuracy and performance for high-dimensional problems. In this article, we mainly solve fully coupled FBSDEs through...

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Bibliographic Details
Published in:IEEE intelligent systems 2020-05, Vol.35 (3), p.71-84
Main Authors: Ji, Shaolin, Peng, Shige, Peng, Ying, Zhang, Xichuan
Format: Article
Language:English
Subjects:
Algorithms
Deep learning
Differential equations
Feedback control
fully-coupled FBSDEs
high-dimensional equation
Intelligent systems
Machine learning
Mathematical analysis
Neural networks
Optimal control
Parabolic differential equations
Partial differential equations
stochastic control
Stochastic processes
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Summary:Recently, the deep learning method has been used for solving forward–backward stochastic differential equations (FBSDEs) and parabolic partial differential equations, as it has good accuracy and performance for high-dimensional problems. In this article, we mainly solve fully coupled FBSDEs through deep learning and provide three algorithms, and the numerical results show remarkable performance, especially for high-dimensional cases.
ISSN:1541-1672
1941-1294
DOI:10.1109/MIS.2020.2971597