Postdoctoral Researcher at PIK and TU Berlin.
Applying Modeling, Simulation and Machine Learning for the Renewable Energy Transition
Google Scholar
- Social class and ecological impacts
- Machine learning for power grid stability
- Efficient simulation of complex networks (NetworkDynamics.jl)
Animation: Learning the coupling function of a complex network using an artificial neural network, DiffEqFlux.jl and NetworkDynamics.jl (developed together with colleagues at PIK) .
Department
Working Group
Contact
14412 Potsdam
- Social class and ecological impacts
- Machine learning for power grid stability
- Efficient simulation of complex networks (NetworkDynamics.jl)
Donner, R. V., Lindner, M., Tupikina, L., & Molkenthin, N. (2019). Characterizing flows by complex
network methods. In A mathematical modeling approach from nonlinear dynamics to complex systems
(pp. 197–226). Springer.
Lindner, M., & Hellmann, F. (2019). Stochastic basins of attraction and generalized committor
functions. Physical Review E, 100(2), 022124.
Lindner, M., & Donner, R. V. (2017). Spatio-temporal organization of dynamics in a two-dimensional
periodically driven vortex flow: A lagrangian flow network perspective. Chaos: An Interdisciplinary
Journal of Nonlinear Science, 27(3), 035806.
Büttner, A., Würfel, H., Plietzsch, A., Lindner, M., & Hellmann, F. (2022). An open source software
stack for tuning the dynamical behavior of complex power systems. 2022 Open Source Modelling and
Simulation of Energy Systems (OSMSES), 1–6.
Nauck, C., Lindner, M., Schürholt, K., & Hellmann, F. (2022). Towards dynamical stability analysis of
sustainable power grids using graph neural networks. NeurIPS ClimateChange (accepted).
Nauck, C., Lindner, M., Schürholt, K., Zhang, H., Schultz, P., Kurths, J., Isenhardt, I., & Hellmann, F.
(2022). Predicting basin stability of power grids using graph neural networks. New Journal of Physics,
24(4), 043041.
Lindner, M., Lincoln, L., Drauschke, F., Koulen, J. M., Würfel, H., Plietzsch, A., & Hellmann, F. (2021).
NetworkDynamics. jl—Composing and simulating complex networks in Julia. Chaos: An
Interdisciplinary Journal of Nonlinear Science, 31(6), 063133.
Schuster, A., Lindner, M., & Otto, I. M. (2023). Whose House is on Fire? Identifying
Socio-Demographic and Housing Characteristics Driving Differences in the UK Household Co2
Emissions. Ecological Economics (accepted).
Clisciety - climate science, energy transition and society
The workshop collective clisciety aims at a constructive exchange between climate science and society. We develop educational workshops and talks on climate change and its consequences and establish contact with potential speakers for your event (in German and English).
Zehn Fakten zum Klimawandel - ZEIT online, with Antonia Schuster
I offer courses for beginners in programming (Python, in German and English). Contact me if you are looking for a facilitator.
