Patrick Cheridito

This paper introduces an iterative neural network-based approach to solve finite-horizon continuous-time stochastic control problems with jumps, when the underlying dynamics are fully known and given.

We introduce a deep learning method for calculating convex conjugates in high-dimensional settings, significantly enhancing computational efficiency and providing L2-convergence certificates.