backward stochastic differential equations

Wednesday, May 9, 2018 - 4:00pm - 4:30pm
Jiequn Han (Princeton University)
Developing algorithms for solving high-dimensional stochastic control problems and high-dimensional partial differential equations (PDEs) has been an exceedingly difficult task for a long time, due to the notorious difficulty known as the curse of dimensionality. In the first part of this talk, we develop a deep learning-based approach that directly solves high-dimensional stochastic control problems based on Monte-Carlo sampling.
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