Archive

Here you find the list of talks in the MODUS Seminar from past semesters. The list goes back to 2023, for earlier talks please see the MODUS elearning course.

Winter Semester 2023/2024

29.11.2023, 12:15h
Mathias Oster
Institute for Geometry and Practical Mathematics, RWTH Aachen University
Empirical Tensor Train Approximation in Optimal Control
Abstract: We display two approaches to solve finite horizon optimal control problems. First we solve the Bellman equation numerically by employing the Policy Iteration algorithm. Second, we introduce a semiglobal optimal control problem and use open loop methods on a feedback level. To overcome computational infeasability we use tensor trains and multi-polynomials, together with high-dimensional quadrature, e.g. Monte-Carlo. By controlling a destabilized version of viscous Burgers and a diffusion equation with unstable reaction term numerical evidence is given.

13.12.2023, 12:15h
Johannes Margraf
Artificial Intelligence in Physico-Chemical Material Analysis, Universität Bayreuth
Designing Molecules and Materials with Machine Learning

10.1.2024, 12:15h
Rainer Hegselmann
Frankfurt School of Finance & Management
Two-armed bandits versus Carnapian truth seekers and epistemic free riders with bounded confidence

17.1.2024, 12:15h
Agnes Koschmider
Wirtschaftsinformatik und Process Analytics, Universität Bayreuth
How to efficiently pre-process unstructured data for process mining?
Abstract: Process mining is a promising approach to find additional patterns in data and in that way to give new insights into the data. The challenge of process mining on unstructured data is to efficiently pre-process the data in a way that process mining can give additional insights. If the data is not clustered appropriately, the result might be distorted (i.e., there is a correlation between clustering and the discovered process model). This talk presents approaches for change point detection and encodings allowing to divide the pre-processed data representative for process mining.

31.1.2024, 12:15h
Mario Sperl
Angewandte Mathematik, Universität Bayreuth
Curse-of-dimensionality-free approximations of optimal value functions with neural networks

7.2.2024, 12:15h
Dominik Kamp
Wirtschaftsmathematik, Universität Bayreuth
Nachfragedynamische Erweiterungen für das Stochastic Guaranteed Service Model auf realistischen Lagernetzen

Summer Semester 2023

10.5.2023, 12:15h
Janosch Hennig
Chair of Biochemistry IV - Biophysical Chemistry, Universität Bayreuth
AI-driven revolutions in structural biology: a new dawn for biomolecular NMR spectroscopy

17.5.2023, 12:15h
Janin Henkel-Oberländer
Chair of Nutritional Biochemistry, Universität Bayreuth
Challenges in histological tissue analysis

31.5.2023, 12:15h
Rainer Hegselmann
Frankfurt School of Finance & Management
Bounded Confidence Revisited

14.6.2023, 12:15h
Karl Worthmann
TU Ilmenau
Data-based prediction of dynamical (control) systems

28.6.2023, 12:15h
Athanasios Antoulas
Rice University, Houston, USA
Interpolatory methods for model reduction and the Loewner framework

5.7.2023, 12:15h
Ruben Mayer
Lehrstuhl für Data Systems, Universität Bayreuth
Recent Advances in Graph Partitioning for Increasing the Performance of Large-Scale Distributed Graph Processing
Abstract: Graph-structured data is found in various domains such as social networks, websites, and recommendation networks. To analyze large graphs and gain high-level insights, distributed graph processing frameworks such as Spark/GraphX and Giraph have been established. For distributed processing, the graph needs to be split into multiple partitions, while the cut size and balancing of the partitions need to be optimized. This problem is known as graph partitioning.
In this talk, I will summarize recent advances of graph partitioning and introduce important new concepts that have been developed in my group. First, two novel techniques that reduce the memory footprint of graph partitioning while maintaining a high partitioning quality: Hybrid Edge Partitioning and Two-Phase Streaming. Second, EASE, a framework for optimizing the choice of partitioning technique for a given graph and processing algorithm. EASE is based on machine learning and achieves better performance than a manual partitioner selection based on heuristics. Finally, I will provide an outlook on open problems.

12.7.2023, 12:15h
Thomas Bocklitz
AG Künstliche Intelligenz in der Spektroskopie und Mikroskopie, Universität Bayreuth
AI for spectroscopy and microscopy: inverse modelling and data modelling tasks

19.7.2023, 12:15h
Michael Wilczek
Lehrstuhl Theoretische Physik I, Universität Bayreuth
Insights into turbulence from fully resolved simulations
Abstract: Fluid turbulence plays an important role in nature and engineering processes. Despite its importance, many aspects still remain to be understood. From a physics perspective, one challenge is to derive theories of turbulence which allow us to understand and predict nontrivial statistical features of turbulence such as the frequent occurrence of extreme events. Fully resolved turbulence simulations provide a useful framework to investigate the spatio-temporal properties of turbulence. In this presentation, I will discuss some recent works which demonstrate how theoretical modeling and simulations can be combined to better understand fundamental aspects of turbulence.