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Complexity Science

Machine Learning, Nonlinear Methods and Decision Strategies

Our Research Department Complexity Science applies and develops methods to understand all aspects of the global climate and sustainability problem. The research ranges from physical analysis of extreme weather events to network analysis of societal phenomena.


Examples of RD-4's lines of research:

  • Data-based and theoretical analysis of institutional and human decisions related to climate change
  • Numerical modelling of climate impacts on  dynamically evolving socio-economic networks
  • The study of emergent dynamics and structure formation in complex networks as a novel approach to model heterogeneous climate impacts and interacting social and infrastructure systems
  • The development of methods of nonlinear time series analysis, machine learning and visualization techniques and their application to observations and management of the system Earth with special emphasis on extreme events
  • The development of methods for the visual communication and analysis of climate data, for decision making under uncertainty and for model verification

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