Christian Nauck

RWTH Aachen University

10/2016 - 09/2020 M.Sc. Aeronautical Engineering and Astronautics, Focus: Aviation

10/2017 - 04/2020 M.Sc. General Mechanical Engineering, Focus Simulation Technology

10/2012 - 08/2016 B.Sc. Mechanical Engineering

My interests are:

• Power grids
• Machine Learning
• Graph Neural Networks

Under review

• C. Nauck et al. (2024): Predicting Instability in Complex Oscillator Networks: Limitations and Potentials of Network Measures and Machine Learning, pre-print http://arxiv.org/abs/2402.17500
• C. Nauck et al. (2024): Predicting Fault-Ride-Through Probability of Inverter-Dominated Power Grids using Machine Learning, arXiv:2406.08917

Peer-reviewed

• Raum, H., Schnake, T., Hellmann, Frank, Kurths, Jürgen, Nauck, Christian (2025): Explainable AI for analyzing the decision of GNNs at predicting dynamic stability of complex oscillator networks - Chaos, https://doi.org/10.1063/5.0278469
• Zhu, Junyou, Nauck, Christian, Lindner, Michael, He, Langzhou, Yu, Philip S., Müller, Klaus-Robert , Kurths, Jürgen, Hellmann, Frank (2025): Network Measure-Enriched GNNs: A New Framework for Power Grid Stability Prediction - IEEE Transactions on Knowledge and Data Engineering https://doi.org/10.1109/TKDE.2025.3624222
• C. Nauck et al. (2024): Dirac--Bianconi Graph Neural Networks -- Enabling Non-Diffusive Long-Range Graph Predictions, accepted at Proceedings of the Geometry-grounded Representation Learning and Generative Modeling at ICML 2024, arXiv:2407.12419
• C. Nauck et al. (2023): Toward dynamic stability assessment of power grid topologies using graph neural networks, Chaos: An Interdisciplinary Journal of Nonlinear Science, 10.1063/5.0160915
• J. Biehl et al. (incl. C. Nauck) (2023): Wicked facets of the German energy transition – examples from the electricity, heating, transport, and industry sectors. International Journal of Sustainable Energy, 10.1080/14786451.2023.2244602
• C. Nauck et al. (2022): Towards dynamic stability analysis of sustainable power grids using graph neural networks, NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning, 10.48550/arXiv.2206.06369
• C. Nauck et al. (2022): Predicting basin stability of power grids using graph neural networks, New J. Phys. 10.1088/1367-2630/ac54c9

• KI-FounDyn from March 2024 until December 2024 (https://www.pik-potsdam.de/en/output/projects/all/1018)
• Explainable AI for dynamic stability assessment (eKI4DS) from January 2024 until December 2027

scholarship by DBU (2020-2023)