About us


Interdisciplinarity of Computational Electromagnetics
Interdisciplinarity of Computational Electromagnetics

The chair of Computational Electromagnetics (CEM) is part of the institute TEMF and the Centre for Computational Engineering. Teaching and research focuses on the third pillar of understanding: computer simulation. Besides theory and observation it can give answers to questions from engineering and science.

Mission statement

Electrotechnical systems are becoming more and more complex. Innovative devices are designed closely to what is technically and physically possible. Consequently, the theory required to analyze the corresponding systems is becoming increasingly involved as well. Experimental investigations are often too complex, too risky, or too costly and the presence of test probes might corrupt the experiment data. Computational Electromagnetics is in those cases the most appropriate way to gain knowledge.

Computer based modeling, analysis, simulation, and optimization, are a cost effective and efficient alternative to investigate real-world applications and to engineer new technical solutions. The digital models (`virtual prototypes') support research, development, design, construction, evaluation, production, and give insight into the operation of engineering applications. It allows us to find optimal strategies which address key issues in future technical developments both for the economy and for society in areas such as energy, health, safety, and mobility.

The main subject of research and teaching at the chair is the modeling and simulation of electromagnetic and multiphysical phenomena by means of numerical solutions of partial differential equations and in particular of Maxwell's equations. We are working on all the development stages, mainly on modeling and the development of numerical algorithms, but also on real-world applications. For this work existence, uniqueness and differentiability of solutions, robustness, convergence and scalability of the algorithms are as important as their efficient implementation, e.g., acceleration of numerical linear algebra by Graphics Processing Units (GPUs).