Dr. Felix Dietrich
Emmy Noether Junior Research Group
Harmonic Artificial Intelligence based on Linear Operators
Chair of Scientific Computing in Computer Science
Academic Career and Research Areas
Dr. Dietrich studied Scientific Computing (B.Sc.) at the University of Applied Sciences, Munich and the KTH Royal Institute of Technology in Stockholm, Sweden. He obtained his Masters (2014) and PhD (2017) in Mathematics from the Technical University of Munich, in the Elite Program TopMath. Between 2017 and 2019, he was a Postdoctoral Fellow in the Department of Chemical and Biomolecular Engineering, Whiting School of Engineering at Johns Hopkins University, and a Visiting Research Collaborator at Princeton University, working with Prof. Kevrekidis. In 2019, Dr. Dietrich joined TUM to work as a research associate at the chair of Scientific Computing. Since 2022, he leads his own DFG Emmy Noether Junior Research group at the Technical University of Munich.
Dr. Dietrich (b. 1989) conducts research in the analysis and development of numerical algorithms for machine learning. This includes algorithms to enable, accelerate, and optimize simulation and analysis of complex dynamical systems, nonlinear manifold learning techniques, and the connection of neural networks with Gaussian processes. His group focuses on kernel methods and data-driven approximations of Koopman and Laplace operators.
Awards
- Emmy Noether Junior Research Group of the German Research Foundation (2021)
- Scholarship of the German Academic Scholarship Foundation (2009-2014)
- Scholarship of the Max Weber-Program of the state of Bavaria (2009-2014)
Key Publications (all publications)
E. Peterfreund, O. Lindenbaum, F. Dietrich, T. Bertalan, M. Gavish, I. G. Kevrekidis, and R. R. Coifman. "Local conformal autoencoder for standardized data coordinates". In: Proceedings of the National Academy of Sciences (Nov. 2020), p. 202014627.
AbstractF. Dietrich, T. N. Thiem, and I. G. Kevrekidis. "On the Koopman Operator of Algorithms". In: SIAM Journal on Applied Dynamical Systems 19.2 (Jan. 2020), pp. 860–885.
AbstractT. Bertalan, F. Dietrich, I. Mezic, and I. G. Kevrekidis. "On learning Hamiltonian systems from data". In: Chaos 29.12 (Dec. 2019), p. 121107
AbstractQ. Li, F. Dietrich, E. M. Bollt, and I. G. Kevrekidis. "Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator". In: Chaos 27.10 (Oct. 2017), p. 103111.
AbstractF. Dietrich, G. Köster, and H.-J. Bungartz. "Numerical Model Construction with Closed Observables". In: SIAM Journal on Applied Dynamical Systems 15.4 (Nov. 2016), pp. 2078–2108.
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