Kaiserslautern Applied and Industrial Mathematics Days

Conference with Focus on: Combination of Model-Based Approaches and Data-Driven Techniques will take place in Kaiserslautern, Fraunhofer ITWM, Germany, from September 25-27, 2023. The purpose of this conference is to provide a common platform for mathematicians from academia, research laboratories and industry at the site to exchange ideas and present current results. The format is on a bi-annual basis and has an international focus. The focus of the second edition of KLAIM is on the synthesis of models and data. The workshop is jointly hosted and organized by the Fraunhofer Institute for Industrial Mathematics ITWM and the Department of Mathematics at TU Kaiserslautern. Registration and further details will be announced in April 2023. Find out more at: www.itwm.fraunhofer.de/klaim2023-en If you have any questions please contact us: KLAIM@mathematik.uni-kl.de We are looking forward to seeing you in Kaiserslautern!

Plenary Speakers

We were successful in attracting the following speakers from across the disciplines:

• Prof. Dr. Peter Benner (Max-Planck-Institut Magdeburg, Fields of Activity Computational Methods in Systems and Control Theory)
• Dr. Robert Bixby (Rice University, Fields of Activity Linear and Integer Optimization)
• Prof. Dr. Gitta Kutyniok (Ludwig Maximilians Universität München, Fields of Activity Mathematical Foundations of Artificial Intelligence)
• Assoz. Prof. Mag. Dr.techn Ivana Ljubic (ESSEC Business School and Academic Director for Executive MBA-Programs, Professor for Operations Research)
• Prof. Dr. rer. nat. Matthias Scherer (Technische Universität München, Professur for Risk and Insurance)

Program and Tracks

The program will be structured along the five tracks:

1. Models and Data across Scales and Domains in Engineering Applications
2. Risk Management and Machine Learning
3. Simulation and Optimization in Fluid Dynamics
4. Mathematical Programming: Uncertain Data and Multiple Objectives
5. Analyzing Materials Structures: Images, Machine Learning and Stochastic Geometry