It is one of the most challenging problems in applied mathematics to approximatively solve
high-dimensional partial differential equations (PDEs). Recently, several deep learning …

Abstract Backward Stochastic Differential Equations (BSDEs) have been widely employed in
various areas of social and natural sciences, such as the pricing and hedging of financial …

The estimation of repeatedly nested expectations is a challenging task that arises in many
real-world systems. However, existing methods generally suffer from high computational …

Backward stochastic differential equations (BSDEs) belong nowadays to the most frequently
studied equations in stochastic analysis and computational stochastics. BSDEs in …

Discrete time stochastic optimal control problems and Markov decision processes (MDPs),
respectively, serve as fundamental models for problems that involve sequential decision …

Life cycle asset allocation is a crucial aspect of financial planning, especially for pension
funds. Traditional methods often face challenges in computational efficiency and …

Discrete time stochastic optimal control problems and Markov decision processes (MDPs),
respectively, serve as fundamental models for problems that involve sequential decision …

The general framework of sequential decision-making captures various important real-world
applications ranging from pricing, inventory control to public healthcare and pandemic …

Classical numerical approximation methods for partial differential equations typically suffer
from the so-called curse of dimensionality. This means that the computational effort for …