Quantifying and reducing uncertainties in earth fluid models

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Project Description

Earth fluid models are subject to different sources of uncertainties. We will work on developing and implementing Bayesian inference approaches to quantify and reduce uncertainties in these models with focus on applications related to the coastal ocean, e.g. storm surges, tsunamis, oil spill, waves, etc. We envision using statistical and polynomial chaos-based techniques to build surrogate models that can be used to reduce the computational burden of the sampling step in the Bayesian inference.  ​​​​​
Program - Earth Science and Engineering
Division - Physical Sciences and Engineering
Field of Study - Applied Mathematics, Earth Sciences and Engineering, or any related field

About the
Researcher

Ibrahim Hoteit

Professor, Earth Science and Engineering

Ibrahim Hoteit

My research involves the effective use and integration of dynamical models and observations to simulate, study and predict realistic geophysical fluid systems. This involves developing and implementing numerical models and data inversion, assimilation, and uncertainty quantification techniques suitable for large scale applications. I am currently focusing on developing an integrated data-driven modeling system to simulate and predict the circulation and the climate of the Saudi marginal seas: the Red Sea and the Arabian Gulf.