Stochastic Control

Prof. Nacira Agram

Deep Learning Methods for Backward Stochastic Volterra Integral Equations with Applications

Prof. Nacira Agram, Department of Mathematics, KTH Royal Institute of Technology, Stockholm, Sweden

Mar 31, 13:00 - 14:00

Deep learning Stochastic Control

Abstract Backward stochastic Volterra integral equations (BSVIEs) provide a natural framework for modeling stochastic systems with memory and path dependence. Such equations arise in several areas including recursive utilities, stochastic control, rough volatility models, and path-dependent partial differential equations. Despite their importance, numerical methods for BSVIEs remain relatively limited in the literature due to the presence of bi-temporal processes and the resulting analytical and computational challenges. In this talk, I present a deep learning framework for approximating the

Elsiddig Awadelkarim Elsiddig

Postdoctoral Research Fellow, Statistics

MCMC Particle Methods Stochastic Control machine learning stochastic partial differential equations