The study of stochastic differential equations (SDEs) has long been a cornerstone in the modelling of complex systems affected by randomness. In recent years, the extension to G-Brownian motion has ...

Backward Stochastic Differential Equations (BSDEs) constitute a class of stochastic processes where the solution is determined by a prescribed terminal condition rather than an initial one. These ...

This paper presents a novel and direct approach to solving boundary- and final-value problems, corresponding to barrier options, using forward pathwise deep learning and forward–backward stochastic ...

This is a preview. Log in through your library . Abstract This paper analyzes a noncooperative and symmetric dynamic game where players have free access to a productive asset whose evolution is a ...

This is a preview. Log in through your library . Abstract We study the weak solution X of a parabolic stochastic partial differential equation driven by two independent processes: a Gaussian white ...

(Conditional) generative adversarial networks (GANs) have had great success in recent years, due to their ability to approximate (conditional) distributions over extremely high-dimensional spaces.

Brownian motion and Langevin's equation. Ito and Stratonovich Stochastic integrals. Stochastic calculus and Ito's formula. SDEs and PDEs of Kolmogorov. Fokker-Planck, and Dynkin. Boundary conditions, ...

Stochastic processes are at the center of probability theory, both from a theoretical and an applied viewpoint. Stochastic processes have applications in many disciplines such as physics, computer ...

Inhalt: The course “Stochastic Analysis” is for master students who are already familiar with fundamental concepts of probability theory. Stochastic analysis is a branch of probability theory that is ...