Three algorithms for solving high-dimensional fully-coupled FBSDEs through deep learning
This work addresses computational challenges in solving complex FBSDEs, which is important for applications in finance and physics, but it appears incremental as it builds on existing deep learning methods for FBSDEs.
The paper tackles solving high-dimensional fully-coupled forward-backward stochastic differential equations (FBSDEs) using deep learning, proposing three algorithms that demonstrate remarkable performance in numerical results for high-dimensional cases.
Recently, the deep learning method has been used for solving forward-backward stochastic differential equations (FBSDEs) and parabolic partial differential equations (PDEs). It has good accuracy and performance for high-dimensional problems. In this paper, we mainly solve fully coupled FBSDEs through deep learning and provide three algorithms. Several numerical results show remarkable performance especially for high-dimensional cases.