Search Results for author:
Found 7 papers, 0 papers with code
Complexity and Persistence of Price Time Series of the European Electricity Spot Market
Electricity markets are central for the coordination of electric power generation.
The onset of molecule-spanning dynamics in a multi-domain protein
Nano- and picosecond dynamics have been assigned to local fluctuations, while slower dynamics have been attributed to larger conformational changes.
An overview on deep learning-based approximation methods for partial differential equations
It is one of the most challenging problems in applied mathematics to approximatively solve high-dimensional partial differential equations (PDEs).
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).
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.
Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations
The PDEs in such applications are high-dimensional as the dimension corresponds to the number of financial assets in a portfolio.
