Multi-dimensional feature and data mining

In this thesis we explore machine and deep learning approaches that address keychallenges in high dimensional problem areas and also in improving accuracy in wellknown problems. In high dimensional contexts, we have focused on computational fluid dynamics (CFD) simulations. CFD simulations are able to produce complex and large outputs that accurately describe the physical properties of fluids and gases in various domains and they are frequently used for studying the effects of flow pat-terns and design choices on many engineering designs, such as wing, car and engineshapes. Due to the high dimensional aspect of the data, it is difficult to model to-ward achieving critical goals such as optimizing lift and drag forces. The key research question addressed in this thesis is whether we develop automated approaches that accurately abstract this information? We tackle these issues by studying a closely re-lated field, 3D computer vision, and adapt approaches to the particular data type.Moreover, inspired by this data type we propose new, deep learning, approaches that are also applied to traditional computer vision.