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10/24/2022
An efficient Monte Carlo scheme for Zakai equations
In this paper we develop a numerical method for efficiently approximatin...
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05/07/2022
Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions
Nonlinear partial differential equations (PDEs) are used to model dynami...
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02/06/2022
Learning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricing
In financial engineering, prices of financial products are computed appr...
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12/02/2020
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 approximati...
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05/20/2020
Numerical simulations for full history recursive multilevel Picard approximations for systems of high-dimensional partial differential equations
One of the most challenging issues in applied mathematics is to develop ...
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08/05/2019
Solving high-dimensional optimal stopping problems using deep learning
Nowadays many financial derivatives which are traded on stock and future...
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07/08/2019
Deep splitting method for parabolic PDEs
In this paper we introduce a numerical method for parabolic PDEs that co...
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06/01/2018
