Contact Person DRI

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Prof. Dr. Athanasios C. Antoulas
Forschungsgruppenleiter
Phone:+49 391 61 10-450
Diana Noatsch-Liebke
Phone: +49 391 6110 450
Fax: +49 391 6110 526

Press Release

Simulation of large-scale systems: Renowned Professor of Electrical and Computer Engineering strengthens cooperation with the MPI Magdeburg

Prof. Athanasios Antoulas has been named a Max Planck Fellow at Max Planck Institute Magdeburg

June 02, 2017

Simulation of large-scale systems: Renowned Professor of Electrical and Computer Engineering strengthens cooperation with the MPI Magdeburg [more]

Research Groups

Max Planck Fellow Group

The Max Planck Fellows program aims to strengthen cooperations between Max Planck Institutes and universities. University teaching staff can be appointed as Max Planck Fellows for a maximum of five years, during which they also head a small research group at a Max Planck Institute.

Athanasios C. Antoulas, Professor for Electrical and Computer Engineering at RICE University Houston, Texas, USA, has been named a Max Planck Fellow at the Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg by the beginning of 2017, where he will do research in the area of numerical simulation of complex data in his group Data-Driven System Reduction and Identification (DRI) within the next three years.

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.

Data-Driven System Reduction and Identification (DRI)

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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