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Found 6 papers, 1 papers with code
Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions
We evaluate the performance of the two methods on five different PDEs arising in physics and biology.
Deep learning based numerical approximation algorithms for stochastic partial differential equations and high-dimensional nonlinear filtering problems
In this article we introduce and study a deep learning based approximation algorithm for solutions of stochastic partial differential equations (SPDEs).
Pricing and hedging American-style options with deep learning
In this paper we introduce a deep learning method for pricing and hedging American-style options.
Solving high-dimensional optimal stopping problems using deep learning
We present numerical results for a large number of example problems, which include the pricing of many high-dimensional American and Bermudan options, such as Bermudan max-call options in up to 5000 dimensions.
Deep splitting method for parabolic PDEs
In this paper we introduce a numerical method for nonlinear parabolic PDEs that combines operator splitting with deep learning.
Solving the Kolmogorov PDE by means of deep learning
Stochastic differential equations (SDEs) and the Kolmogorov partial differential equations (PDEs) associated to them have been widely used in models from engineering, finance, and the natural sciences.
