Quantum computers have the potential to surpass conventional computing, but they are hindered by noise which induces errors that ultimately lead to the loss of quantum information. This necessitates the development of quantum error correction strategies fo ...
This study aims to identify an optimal, as well as practical, parametric structure for a delta-wing UAV aerodynamic model for the purpose of model-based navigation. We present a comprehensive procedure for characterizing the aerodynamics of this platform, ...
Accessing the thermal transport properties of glasses is a major issue for the design of production strategies of glass industry, as well as for the plethora of applications and devices where glasses are employed. From the computational standpoint, the che ...
We present an orbital-resolved extension of the Hubbard U correction to density-functional theory (DFT). Compared to the conventional shell-averaged approach, the prediction of energetic, electronic and structural properties is strongly improved, particula ...
In this thesis we study stability from several viewpoints. After covering the practical importance, the rich history and the ever-growing list of manifestations of stability, we study the following. (i) (Statistical identification of stable dynamical syste ...
While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that th ...
Colloid particle size plays an important role in contaminant adsorption and clogging in the hyporheic zone, but it remains unclear how the particle size changes during the transport of colloids. This study investigated the variation of the particle size of ...
A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...
This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We stud ...
