Grip on Complexity
Pushing forward the foundations and applications of complexity science
Mastering complexity requires a deep understanding of how systems – created by nature or mankind – are formed through local interactions and self-organizing principles. The Grip on Complexity (GoC) focus area connects mathematics, physics, chemistry, biomedical/biochemical sciences and engineering sciences, with the overarching goal to push forward the foundations and applications of complexity science.
CoG focuses on fundamental research that combines experiments and data analysis with mathematical modeling, analysis and control. It fosters new collaborations and provides impetus for cross-disciplinary complexity research in emerging areas that are likely to have a huge impact in the field of Complex Molecular Systems. The overarching goal is to match experimental data with rigorous underpinning, which will foster the development of novel design, synthesis and control strategies for building complex systems with advanced functionality.
Albeit fundamental in nature, GoC research is expected to generate societal impact and industrial innovation, stimulated through company contacts and real-life experiments. Research in this area focuses on networks and dynamics and control.
The goal in the field of networks (consisting of 'particles' such as humans, devices or molecules) is to further develop mathematical network theory and advanced network science, and to apply novel tools for understanding designing complex networks, including biological networks such as the brain, molecular networks, quantum networks, communications networks and social networks.
In dynamics and control the focus is on modeling complex systems to describe how non-linear properties and scale-dependent behavior emerges from local interaction, and how they give rise to global emergent behavior. Dynamical processes can describe the formation of complex systems over time, be it the construction of a polymer, the growth of the brain or the creation of a social network. The goal is to analyze and control the dynamics of complex systems by designing new theoretical and computational modeling methodologies.