data assimilation

Particle Approximations For Partially Observed Fluid Dynamics Models

Professor Dan Crisan, Mathematics, Imperial College London

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KAUST

Fluid dynamics models are ubiquitous in a multitude of applications. One of the most important applications of fluid dynamics models is numerical weather prediction. Modern numerical weather prediction combines sophisticated nonlinear fluid dynamics models with increasingly accurate high-dimensional data. This process is called data assimilation and it is performed every day at all major operational weather centers across the world. Data assimilation (DA) requires massive computing capabilities as realistic atmosphere-ocean models typically have billions of degrees of freedom. I will give a short overview of the ongoing research that aims to drastically decrease the required DA computational effort by reducing the dimension of the models involved and using stochastic perturbations to account for the unresolved scales. The incorporation of observation data is done by using particle approximations suitably adapted to solve high-dimensional problems.

Is probability theory the only viable solution to modelling statistical information?

Prof. Jeremie Houssineau, Department of Statistics, University of Warwick.

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KAUST

Many alternatives to the probabilistic modelling of information have been proposed since the birth of modern Statistics; yet, few have been successfully applied to the complex inference problems that modern Statisticians are faced with.

Data Assimilation for Flood Real Time Estimation: Challenges and Methods

Prof. Christian Claudel, The University of Texas at Austin

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B2 5220

Abstract Flash floods are one of the most common natural disasters worldwide, causing thousands of casualties every year. A combination of fixed and mobile sensors can be used to track these events. In particular, the emergence of Unmanned Aerial Vehicles (UAVs) could enable the monitoring of floods over large geographical areas. In this talk, we focus on the problems of sensing, data assimilation, and trajectory planning for a swarm of mobile agents (for example UAVs) sensing floods We first show that deep-learning can efficiently approximate the flood evolution over time, given external

Raul Tempone

Professor Raul Tempone is an worlwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Håkon A. Hoel

Håkon Hoel was a visiting researcher in the Stochastic Numerics Group at KAUST. He holds an MSc degree in Computational Science from the University of Oslo (2016) and a Ph.D. in Numerical Analysis from KTH Royal Institute of Technology (2012). His main research interests are numerical analysis of stochastic differential equations, nonlinear filtering, and multilevel Monte Carlo (MLMC) methods. His research interests also include different aspects of implementation and error estimation of stochastic models in the following areas: adaptive weak approximation of stochastic differential equations

Hamza M. Ruzayqat

Research Scientist, Omar Knio Research Group
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.

Habib Toye Mahamadou Kele

Brief Biography Habib Toye is a PhD candidate at King Abdullah University of Science and Technology (KAUST), in the Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE) under the supervision of Professor Ibrahim Hoteit. His research interests include Ensemble ocean forecasting, High performance computing and Data assimilation. Research Interest Habib's research focuses on developing and implementing efficient ensemble Kalman filters for data assimilation into a high resolution model of the Red Sea. He has developed a fault-free DART-MITgcm ensemble assimilation