Deep learning

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

Najmeddine Dhieb

Najmeddine Dhieb

Research Engineer, Electrical and Computer Engineering

Deep learning Generative AI and LLMs artificial intelligence Computer Vision

Aznaur Aliev

M.S. Student

AI Deep learning Computer Vision Reinforcement Learning

Article published by Foundations of Data Science

1 min read · Thu, Sep 8 2022

Nonlinear Filtering Inverse problem conditional expectation weather forecast Deep learning

In August 2022, the paper Machine learning-based conditional mean filter: A generalization of the ensemble Kalman filter for nonlinear data assimilation, by Truong-Vinh Hoang (RWTH Aachen University), Sebastian Krumscheid (Karlsruhe Institute of Technology), Hermann G. Matthies (Technische Universität Braunschweig) and Raul Tempone (KAUST/RWTH Aachen University) was published by Foundations of Data Science. Abstract: This paper presents the machine learning-based ensemble conditional mean filter (ML-EnCMF) — a filtering method based on the conditional mean filter (CMF) previously introduced in