SCCS Colloquium - Nov 28, 2019

Date:	November 28, 2019
Room:	00.08.053
Time:	15:00 - 16:00

Sebastian Walter: Deep Learning for Parameter Reduction on Real-World Topography Data

Master's thesis introduction talk. Sebastian is advised by Anne Reinarz and Lukas Krenz.

Working with large amounts of topographical data can be challenging in many ways, where trying to reduce the parameter space is probably amongst the biggest of such. For further processing of the data, e.g. to run physical earthquake or tsunami simulation models, one would ideally sample from a much lower dimensional parameter space and, thus, greatly reduce computational effort. The scope of the Thesis is to explore several approaches to achieve this reduction, i.e. Autoencoders, Variational Autoencoders, Generative Adversial Networks, and compare their performance.

Keywords: Parameter Reduction, Autoencoder, Variational Autoencoder, VAE-GAN