Thesiss

This dissertation is mainly focused on assimilation of data into hydrodynamic models of water flow in open channel networks which is motivated by the need for accurate flow models in various applications such as emergency response and flood monitoring systems, automated gate sys…

A New Ionospheric Model (ANIMo) based upon the physics of production, loss, and vertical transport has been developed. The model is driven by estimates of neutral composition, temperature and solar flux and is applicable to the mid-latitude regions of the Earth under quiet and m…

In this thesis, we produce a rigorous and quantitative analysis of the errors introduced by finite difference schemes into strong constraint 4D-Variational (4D-Var) data assimilation. Strong constraint 4D-Var data assimilation is a method that solves a particular kind of inverse…

Nowadays data assimilation is an essential component of any effective environmental prediction system. Environmental prediction models are, indeed, initial value problems and their forecast skills highly depend on the quality of their initialization. Data assimilation (DA) seeks…

Complex nonlinear dynamical systems are frequently utilized and play a significant role in the study of natural science and many other fields. This thesis studies dynamical systems both theoretically and numerically with the help of machine learning, quantifying uncertainties in…

Wildland fires pose an increasingly serious problem in our society. The number and severity of these fires has been rising for many years. Wildfires pose direct threats to life and property as well as indirect threats through ancillary effects like reduced air quality due to smo…

Data assimilation in subsurface systems is challenging due to the large number of flow simulations often required, and by the need to preserve geological realism in the calibrated (posterior) models. In this work we present a deep-learning-based surrogate-modeling framework, ref…