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MODUS Seminar Program

Weekly talks in the MODUS Seminar in the winter semester 2023/2024:
Wednesday, 12:15-13:45, Room S102, FAN-B
Individual talks may be held via zoom or at different times, see the information, below.
The list of talks is still incomplete and further talks will be added.

To receive email notifications about upcoming talks, please subscribe to the MODUS elearning course.

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

Board of Directors: Prof. Dr. Jörg Rambau, Prof. Dr. Lars Grüne and Prof. Dr. Vadym Aizinger

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