no code implementations • 7 May 2022 • Victor Boussange, Sebastian Becker, Arnulf Jentzen, Benno Kuckuck, Loïc Pellissier
We evaluate the performance of the two methods on five different PDEs arising in physics and biology.
no code implementations • 2 Dec 2020 • Christian Beck, Sebastian Becker, Patrick Cheridito, Arnulf Jentzen, Ariel Neufeld
In this article we introduce and study a deep learning based approximation algorithm for solutions of stochastic partial differential equations (SPDEs).
1 code implementation • 23 Dec 2019 • Sebastian Becker, Patrick Cheridito, Arnulf Jentzen
In this paper we introduce a deep learning method for pricing and hedging American-style options.
no code implementations • 5 Aug 2019 • Sebastian Becker, Patrick Cheridito, Arnulf Jentzen, Timo Welti
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.
no code implementations • 8 Jul 2019 • Christian Beck, Sebastian Becker, Patrick Cheridito, Arnulf Jentzen, Ariel Neufeld
In this paper we introduce a numerical method for nonlinear parabolic PDEs that combines operator splitting with deep learning.
no code implementations • 1 Jun 2018 • Christian Beck, Sebastian Becker, Philipp Grohs, Nor Jaafari, Arnulf Jentzen
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.