Abstract
Forward-backward doubly stochastic differential equations (FBDSDEs) are related to a type of quasi-linear parabolic stochastic partial differential equations (SPDEs). We propose a deep learning-based numerical algorithm for solving such equations. Using deep neural networks as approximations of the controls, we can deal with high-dimensional cases. Numerical experiments are carried out to demonstrate the accuracy and efficiency of the proposed numerical algorithm.
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