Master Thesis MSTR-2022-108

BibliographyNguyen, Hai Dang: Visual exploration for deep learning models and trainings for microstructure data.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Master Thesis No. 108 (2022).
47 pages, english.

Artificial neural networks have become a staple in machine learning research and are employed in many interdisciplinary domains. Thus, it is paramount to understand their inner workings when designing and developing new models. One field of research that is particular useful is called visual analytics, which combines interactive visual representations and data analysis algorithms to obtain knowledge. The aim of this thesis is the development of a visual analytics system to analyze the training behavior of a machine learning model predicting microstructure material responses. The goal is to enable the user to explore how different training configurations influence the training process and the model’s performance. In addition, a novel regularization technique and a novel optimization improvement, greedy stochastic permutations, are proposed.

Full text and
other links
Department(s)University of Stuttgart, Institute of Visualisation and Interactive Systems, Visualisation and Interactive Systems
Superviser(s)Weiskopf, Prof. Daniel; Hägele, David; Lißner, Julian; Munz, Tanja
Entry dateJune 14, 2023
   Publ. Computer Science