Inverse problem

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

Lilia Ghaffour

Ph.D. Student, Applied Mathematics and Computational Sciences

Fractional calculus Control Theory Inverse problem

Research Interests Lilia Ghaffour is a Ph.D. candidate in the Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) at KAUST. She has a master degree in Applied Mathematics from the University of Mostaganem (Mathematics and Computer Science), Algeria. She joined the Ph.D. degree program in Appl. Math. and Computational Sc. at King Abdullah University of Science and Technology (KAUST) in fall 2016. She is interested in the study of control and inverse problem for fractional partial differential equations. Education Profile August 2016: PhD candidate in the Division

Hamza M. Ruzayqat

Research Scientist, Omar Knio Research Group

monte carlo algorithms uncertainty quantification multilevel methods data assimilation Inverse problem bayesian inference MCMC

Dr. Hamza Ruzayqat is a Research Scientist at KAUST, specializing in Monte Carlo algorithms, data assimilation, and uncertainty quantification, with a background that includes a PhD in Mathematics from the University of Tennessee-Knoxville and various teaching and research positions.