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Professor Felix Dietrich conducts research in the field of physics-enhanced machine learning, focusing on the development and analysis of numerical algorithms that improve simulations of complex dynamical systems. His research taps into advanced machine learning methods such as nonlinear manifold learning and Gaussian processes, thereby enhancing predictive modeling capabilities. His group specializes in kernel methods and data-driven approximations, particularly using Koopman and Laplace operators to advance the understanding of dynamical phenomena. Dietrich holds a Bachelor’s degree in Scientific Computing from the University of Applied Sciences Munich and KTH Stockholm, as well as a Master’s (2014) and PhD (2017) in Mathematics from the Technical University of Munich. Following his doctoral studies, he worked as a postdoc at Johns Hopkins University and Princeton University, collaborating with Professor Kevrekidis. In 2019, he returned to TUM to lead the chair of Scientific Computing, and he has been leading a DFG Emmy Noether Junior Research Group since 2022. His career includes a significant appointment in 2024 as a professor in physics-enhanced machine learning at TUM, with impactful contributions to conferences and journals in applied dynamical systems and machine learning.
Johns Hopkins University and Princeton University • United States
Collaborated with Prof. Kevrekidis on projects involving numerical and analytical techniques in applied dynamical systems.
DFG Emmy Noether Junior Research Group • Technical University of Munich
Leading a research group on physics-enhanced machine learning focusing on advanced data-driven techniques.