Advancing Artificial Neural Network Parameterization for Atmospheric Turbulence Using a Variational Multiscale Model

Data-driven parameterizations offer considerable potential for improving the fidelity of General Circulation Models. However, ensuring that these remain consistent with the governing equations while still producing stable simulations remains a challenge. In this paper, we propose a combined Variational-Multiscale (VMS) Artificial Neural...

Football activity recognition: A deep learning approach to football activity recognition based on Inertial Measurement Units signals

been given to Human Activity Recognition (HAR) based on signals obtained by IMUs placed on different body parts. This thesis studies the usage of Deep Learning-based models to recognize different football activities in an accurate, robust, and fast manner. Several deep architectures were trained with data captured with IMU sensors placed on...

Low-Thrust Interplanetary Trajectory Optimization Using Pre-Trained Artificial Neural Network Surrogates

The use of low-thrust propulsion for interplanetary missions requires the implementation of new methods for the preliminary design of their trajectories. This thesis proposes a method using the Monotonic Basin Hopping global optimization algorithm to find feasible trajectories with optimum use of the mass of fuel for the case in which the...

Humble Giants: Computational Intelligence for Designing More Sustainable High-rise Buildings using Surrogate Models

As urbanization increases around the world, high-rise buildings will continue to become a more prevailing typology, nonetheless, due in part to cumbersome computational simulations, rarely do designers have enough information during the early stages of design, which is the time when their choices affect the most the efficiency of their building....

An Integrated Machine Learning and Finite Element Analysis Framework, Applied to Composite Substructures including Damage

Engineering fields such as aerospace rely heavily on the Finite Element Method (FEM) as a modelling tool. In combination with the scale and complexity of the structures typically involved here, computational cost remains a traditional issue. To perform FEM analyses of such structures efficiently nonetheless, engineers rely on techniques such as...