Interdisciplinary Applications

A lot of the work done in the Centre for Complex Systems is relevant in an interdisciplinary context, dealing with the mathematical and statistical description of particular examples of real-world systems in biology, medicine, social sciences, environmental sciences, physics and engineering. We are employing data-driven approaches and performing simulations and predictions to understand the statistical properties and time evolution of real-world complex systems on various time scales.   

For example, we analyse data related to environmental problems, such as the dynamics of air pollution and the fluctuating water quality in rivers. Air pollution accounts for a huge amount of premature deaths worldwide. Our centre was successful in attracting policy impact grants that deal with the statistics of air pollution and discuss the medical consequences (joint with collaborators from the Medical School and the School of Geography). Other interdisciplinary data-driven research includes Covid 19 modelling, cancer evolution, species-interaction in biological systems, complex diffusion processes in biology, movement ecology, research on team dynamics and success, quantitative urbanism, smart cities, sustainable energy systems, power grids, and much more (see also impact section). 

Measured frequency fluctuations around 50Hz in the European power grid (figure from B. Schaefer, C. Beck, K. Aihara, D. Witthaut, M. Timme, Nature Energy 3, 119 (2018))

The measured path of crawling kidney cells (work by R. Klages et al.)