Max Planck Fellow Groups at MPI Magdeburg
The Max Planck Fellow program, initiated in 2005, aims to strengthen cooperation between Max Planck Institutes and universities.
With this initiative, the Max Planck Society strengthens the cooperation of its institutes with universities and promotes synergy effects in scientific excellence and the clustering of research resources.
University teaching staff can be appointed as Max Planck Fellows for a maximum of five years, during which they also head a research group at a Max Planck Institute.
Prof. Dr. rer. nat. habil. Sebastian Sager, professor at Otto von Guericke University Magdeburg, has been appointed Max Planck Fellow and will head the Mathematical Optimization and Machine Learning research group at the Max Planck Institute for Dynamics of Complex Technical Systems for five years
starting October 1, 2023.
The group's main focus is on the application driven development of optimization methods, the close connection to machine learning, and an efficient implementation on computers. Main technologies and fields of expertise comprise nonlinear and integer optimization, as well as optimal control. A specialization is in numerical algorithms for mixed-integer optimization with differential equations. Most of our mathematical models arise from applications from chemical engineering and Power 2 X processes.
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2017 - 2022Athanasios C. Antoulas, Professor for Electrical and Computer Engineering at RICE University Houston, Texas, USA, was appointed a Max Planck Fellow at the Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg by the beginning of 2017, where he did research in the area of numerical simulation of complex data in his group
Data-Driven System Reduction and Identification (DRI) until 2022.
Dynamical systems are a principal tool in the modeling, prediction, and control of physical phenomena ranging from heat dissipation in complex microelectronic devices, to vibration suppression in large wind turbines, to storm surges before an advancing hurricane. Direct numerical simulation may be the only possibility for accurate prediction or control of such complex phenomena. However, an ever-increasing need for improved accuracy requires inclusion of more detail at the modeling stage, leading inevitably to larger-scale, more complex dynamical systems. Such systems are often linked to spatial discretization of underlying time-dependent systems of coupled partial differential equations, and their simulation can create large demands on computational resources.
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