Semantische Suche

Finite Elemente Analysis (FEM) enables the simulation of a material deforming under external forces, but it comes with very high computational costs. This makes it unfeasible to conduct a large number of simulations with varying parameters. In this thesis, we use neural network based sequence models to build a data-driven surrogate model that predicts stress, strain and dislocation density curves produced by an FEM-simulation based on the simulation’s input parameters.

To define “change points”, we assume that there is a distribution, which may change over time, generating the data we observe. A change point then is a change in this underlying distribution, i.e., the distribution coming before a change point is different from the distribution coming after. The principled way to compare distributions, and thus to find change points, is to employ statistical tests.

While change point detection is an unsupervised problem in practice, i.e., the data is unlabeled, the development and evaluation of data analysis algorithms requires labeled data. Only a few labeled real-world data sets are publicly available, and many of them are either too small or have ambiguous labels. Further issues are that reusing data sets may lead to overfitting, and preprocessing may manipulate results. To address these issues, Burg et al. publish 37 data sets annotated by data scientists and ML researchers and assess 14 change detection algorithms on them. Yet, there remain concerns due to the fact that these are labeled by hand: Can humans correctly identify changes according to the definition, and can they be consistent in doing so?

An essential aspect of tabular data is its heterogeneous structure, as it often comes in ``mixed data´´, i.e., it contains both categorical and numerical attributes. Most machine learning methods require the data to be purely numerical. The usual way to deal with this is a categorical encoding.

In this thesis, we evaluate a proposed tabular data synthesis pipeline consisting of a categorical encoding, followed by data synthesis and an optional relabeling of the synthetic data by a complex model. This synthetic data is then used to train a simple model. The performance of the simple model is used to quantify the quality of the generated data. We surveyed the current state of research in categorical encoding and tabular data synthesis and performed an extensive benchmark on a motivated selection of encoders and generators.