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MS23: Data-Driven Optimization

Technical improvements of the recent years allow for many new possibilities in data collection and storage. On the one hand, data is used to optimize industry processes and enhance efficiency. On the other hand, many researchers dedicate to analyze the (big)data sets and employ them for optimization or parameter identification. In this way existing models and processes can be improved and in turn optimization of various industrial applications help to make real life more secure.

With this minisymposium we aim to provide a platform for researchers working with data-driven models and optimization to present state-of-the-art research and connect them to industry. Moreover, we hope to stimulate discussions leading to new research directions and hopefully foster new collaborations among academia and industry.

Wednesday, April 14, 10:20-12:00

  1. 10:20 Kathrin Klamroth, Malena Reiners, Michael Stiglmayr, Multiobjective Optimization for Neural Network Training
  2. Oliver Kolb
  3. 10:45 Jan Pablo Burgard, Carina Moreira Costa, Martin Schmidt, Decomposition Methods for Robustified k-Means Clustering Problems: If Less Conservative Does Not Mean Less Bad
  4. Luca Schäfer

Wednesday, April 14, 14:00-15:40

  1. 14:00 Simon Gottschalk, Matthias Gerdts, Michael Burger, A Deep Reinforcement Learning Based Optimal Control Framework
  2. 14:25 Friedrich Philipp, Patrick Mäder, Johannes Viehweg, Karl Worthmann, Echo State Networks for Multi-Step Prediction of Chaotic Time Series
  3. 14:50 Dieter Armbruster, Michael Herty, Giuseppe Visconti, On Continuum Limits of the Ensemble Kalman Filter
  4. 15:15 Jennifer Weißen, Simone Göttlich, Claudia Totzeck, Space Mapping Optimization for Interacting Particle Systems

Wednesday, April 14, 16:00-17:40

  1. 16:00 Michael Burger, Stefan Steidel, Data-Driven Methods for Inverse Problems in Vehicle Engineering
  2. 16:25 Tristan Gally, Peter Groche, Florian Hoppe, Anja Kuttich, Alexander Matei, Marc E. Pfetsch, Martin Rakowitsch, Stefan Ulbrich, Identification of model uncertainty via optimal design of experiments applied to a mechanical press
  3. 16:50 David Sommer, Martin Eigel, Robust Nonlinear Model Predictive Control using Tensor Networks
  4. 17:15 Claudia Totzeck, Simone Göttlich, Optimal control for interacting particle systems driven by neural networks

Thursday, April 15, 10:20-12:00

  1. 10:20 Philipp Guth, Claudia Schillings, Simon Weissmann, Neural Network based One-shot Inversion
  2. 10:45 Stephan Schmidt, Lukas Baumgärtner, Roland Herzog, Ronny Bergmann, José Vidal-Nuñez, Total Variation Regularization of 3D Inverse Problems of Unknown Geometries
  3. 11:10 Luca Mechelli, Jan Rohleff, Stefan Volkwein, Data-Driven Modeling and Control of Complex Dynamical Systems Arising in Dialysis Treatments
  4. 11:35 Zhomart Turarov, Parameter Calibration of an Anisotropic Interaction Model for Pedestrian Dynamics