Learning the coupling function of a complex network using an artificial neural network, NetworkDynamics.jl (developed together with colleagues at PIK) and DiffEqFlux.jl.
Department
Working Group
Contact
14412 Potsdam
- Social determinants of household carbon emissions
- Probabilistic models for decentral energy planning
- Machine learning for power grid stability
- Efficient simulation of complex networks (NetworkDynamics.jl)
Nauck, C., Lindner, M., Schürholt, K., Zhang, H., Schultz, P., Kurths, J., ... & Hellmann, F. (2022). Predicting basin stability of power grids using graph neural networks. New Journal of Physics.
Lindner, Michael, et al. "NetworkDynamics. jl—Composing and simulating complex networks in Julia." Chaos: An Interdisciplinary Journal of Nonlinear Science 31.6 (2021): 063133.
Lindner, M., & Hellmann, F. (2019). Stochastic basins of attraction and generalized committor functions. Physical Review E, 100(2), 022124
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, Cham.
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.
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.
