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Found 2 papers, 1 papers with code
Extensions of the Deep Galerkin Method
We extend the DGM algorithm to solve for the value function and the optimal control \simultaneously by characterizing both as deep neural networks.
Solving Nonlinear and High-Dimensional Partial Differential Equations via Deep Learning
In this work we apply the Deep Galerkin Method (DGM) described in Sirignano and Spiliopoulos (2018) to solve a number of partial differential equations that arise in quantitative finance applications including option pricing, optimal execution, mean field games, etc.
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