Semantische Suche
Freitag, 2. September 2022, 11:30 Uhr
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Ort: Raum 348 (Gebäude 50.34)
Webkonferenz: https://kit-lecture.zoom.us/j/67744231815
| Vortragende(r) | Benjamin Jochum |
|---|---|
| Titel | Surrogate models for crystal plasticity - predicting stress, strain and dislocation density over time |
| Vortragstyp | Proposal |
| Betreuer(in) | Daniel Betsche |
| Vortragsmodus | in Präsenz |
| Kurzfassung | When engineers design structures, prior knowledge of how they will react to external forces is crucial. Applied forces introduce stress, leading to dislocations of individual molecules that ultimately may cause material failure, like cracks, if the internal strain of the material exceeds a certain threshold. We can observe this by applying increasing physical forces to a structure and measure the stress, strain and the dislocation density curves.
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. |
Freitag, 9. September 2022, 11:30 Uhr
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Ort: Raum 348 (Gebäude 50.34)
Webkonferenz: https://kit-lecture.zoom.us/my/floriankalinke_(292608)
| Vortragende(r) | Moritz Teichner |
|---|---|
| Titel | Standardized Real-World Change Detection Data Defense |
| Vortragstyp | Bachelorarbeit |
| Betreuer(in) | Florian Kalinke |
| Vortragsmodus | in Präsenz |
| Kurzfassung | The reliable detection of change points is a fundamental task when analyzing data across many fields, e.g., in finance, bioinformatics, and medicine.
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? |
Mittwoch, 21. September 2022, 11:30 Uhr
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Ort: Raum 348 (Gebäude 50.34)
| Vortragende(r) | Martin Wittlinger |
|---|---|
| Titel | Identification and refactoring of bad smells in model-based analyses |
| Vortragstyp | Masterarbeit |
| Betreuer(in) | Sandro Koch |
| Vortragsmodus | in Präsenz |
| Kurzfassung | In der modernen Softwareentwicklung sind modellbasierte Analysen weit verbreitet. Software-Metriken wie die Vorhersage der Cache-Nutzung haben heute ein breites Anwendungsspektrum. Diese Analysen bedürfen ebenso wie traditionelle objektorientierte Programme der Pflege. Bad Smells und ihre Auswirkungen in objektorientiertem Quellcode sind gründlich erforscht worden. Dies fehlt bei der modellbasierten Analyse. Wir haben uns mit objektorientierten Bad Smells beschäftigt und nach ähnlichen Problemen in der modellbasierten Analyse gesucht. Schlechte Gerüche in der Analyse sind ein Faktor, der zur Qualität der Analysesoftware beiträgt. Eine geringere Qualität erschwert den Entwicklungsprozess der Analyse. Wir haben zehn neue Bad Smells entdeckt. Wir haben Algorithmen zur Identifizierung und zum Refaktorisieren für sie entwickelt. Wir stellen Implementierungen der Identifizierungsalgorithmen zur Verfügung und bewerten sie an- hand realer Software. Wir haben versucht, Bad Smells in bestehender Analysesoftware wie Camunda zu erkennen. Wir haben diese Bad Smells in den vorhandenen Analysen gefunden. |
Freitag, 23. September 2022, 12:00 Uhr
iCal (Download)
Ort: Raum 348 (Gebäude 50.34)
(Keine Vorträge)
Freitag, 14. Oktober 2022, 10:30 Uhr
iCal (Download)
Ort: Raum 348 (Gebäude 50.34)
Webkonferenz: https://kit-lecture.zoom.us/j/62996772275
| Vortragende(r) | Thomas Frank |
|---|---|
| Titel | Benchmarking Tabular Data Synthesis Pipelines for Mixed Data |
| Vortragstyp | Bachelorarbeit |
| Betreuer(in) | Federico Matteucci |
| Vortragsmodus | in Präsenz |
| Kurzfassung | In machine learning, simpler, interpretable models require significantly more training data than complex, opaque models to achieve reliable results. This is a problem when gathering data is a challenging, expensive or time-consuming task. Data synthesis is a useful approach for mitigating these problems.
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. |