Michael Kraus

Dr. Michael Kraus is a Research Group Leader at the Max Planck Institute for Plasma Physics in Garching, Germany, where he leads the "Geometric Numerical Integration and Reduced Complexity Modelling" group. His work focuses on developing structure-preserving discretization methods for problems in fluid dynamics and plasma physics, with recent applications in model order reduction and scientific machine learning.

Dr. Kraus received his Doctor of Science (summa cum laude) from Technische Universität München in 2013, with doctoral studies in Numerical Methods in Plasma Physics. He completed a Diploma in Physics from TUM (2010) with major in Physics and minor in Scientific Computing, and earlier studied Physics at Friedrich-Schiller-Universität Jena.

His research interests span Numerical Methods for Fluids and Plasmas, Structure Preserving Discretisation Methods, Reduced Complexity Modelling, Scientific Machine Learning, Geometric Mechanics and Field Theory, Symmetries and Conservation Laws, Research Software Engineering, and Scientific Computing with Julia. His work leverages geometric properties of equations to derive accurate and robust numerical schemes that better represent underlying physics while exhibiting good stability properties.

Dr. Kraus's recent publications demonstrate a strong focus on geometric numerical integration techniques applied to plasma physics and fluid dynamics. His work consistently emphasizes structure-preserving methods, with particular attention to variational integrators, metriplectic systems, and applications to magnetohydrodynamics, kinetic theory, and the Vlasov-Maxwell system. His research bridges mathematical theory with practical computational implementations.

Dr. Kraus has received several prestigious awards including:

• ANR-DFG Research Grant (2021)
• Research Grant from International Graduate School of Science and Engineering, TUM (2018)
• Marie Skłodowska-Curie Individual Fellowship (2016)
• Otto Hahn Medal of the Max Planck Society (2014)
• PhD awarded with highest distinction (summa cum laude) (2013)

Dr. Kraus actively mentors graduate students, currently supervising multiple PhD candidates in Mathematics at Technische Universität München. His research is supported by significant grants including the ANR/DFG International Project "MILK: MachIne Learning for reduced Kinetic models" (since 2021) and the IGSSE International Project "GEMKIN: Geometric Methods for Kinetic Equations" (2018-2021).

As lead author of various Julia packages for Geometric Numerical Integration and Reduced Complexity Modelling, Dr. Kraus maintains a strong focus on research software engineering and scientific computing. His group develops computational tools that implement the theoretical numerical methods he researches, creating a tight feedback loop between theory and practice.