SC²S Colloquium - Sep 26, 2018
| Date: | Sep 26, 2018 |
| Room: | 02.07.023 |
| Time: | 15:00 - 15:30 |
Tobias Bernecker: Outlier Detection Techniques using Sparse Grids Density Estimation
This is a Bachelor's Thesis submission talk advised by Paul Sarbu
Outlier detection helps to improve results of a clustering process by identifying noisy, anomalous data points in a dataset. However, lots of techniques for outlier detection require a density estimation of the data points, which cannot be computed exactly. To deal with this problem, spatially adaptive sparse grids can be used to learn and approximate the underlying density function of a multi-dimensional dataset. After this learning process, also known as sparse grids density estimation, the obtained approximated function can be evaluated at every data point to receive a corresponding density value. In this thesis, several outlier detection techniques including the Local Outlier Factor and the Local Density Factor are presented. Furthermore, a new density- based approach to obtain a factor for the outlierness of a data point is introduced. The purpose of this thesis is to assess whether outlier detection is a suitable field of application for sparse grids density estimation. To this end, this approach is integrated into an outlier detection framework allowing comparison to other known methods. To validate the results of the presented outlier detection techniques, several artificial datasets with a certain percentage of outliers are tested. Additionally, real datasets are used for further expirements and analysis of the studied detection methods.
Keywords: Sparse Grids, Density Estimation
Noah Lee: Node-level performance optimization of the ADER-DG method for the simulation of tsunamis
This is a Master's Thesis submission talk advised by Leonhard Rannabauer
sam(oa)^2 is a framework for dynamically adaptive mesh refinement. In this thesis, sam(oa)^2 will be used for the simulation of the shallow water equations using the ADER Discontinuous-Galerkin method (ADER-DG). The focus will be on understanding and evaluating the ADER-DG model and improving node level performance as well as a performance study of scaling across multiple compute nodes.
Keywords: sam(oa)^2, Node-level