Network Dynamics - Challenges for our understanding

Networks are almost everywhere around us: We need the internet to display this text. We need neural networks in our brain to understand it. We need mobility networks to get us to the university and back home.

The goal of our research at the Chair for Network Dynamics is a unifying understanding of the fundamental mechanisms underlying the collective dynamics of all of these large, nonlinear, interconnected systems. We combine first-principles theory with data-driven analysis and modeling to investigate emergent phenomena in a broad range of network dynamical systems. A substantial part of our work focuses on developing conceptually new perspectives on complex systems and the theoretical and computational tools necessary to understand them.

This fundamental understanding forms the basis to predict and eventually control the dynamics of complex networked systems across disciplines.

Collective Dynamics of Sustainable Mobility

Human mobility is changing rapidly, driven by digitalization and emerging technologies. A growing focus on sustainability reshapes mobility services and infrastructures everywhere. Battery-electric vehicles are replacing combustion engines. Digital technologies and autonomous vehicles promise to enable new forms of shared mobility.

 

In the research team Collective Dynamics of Sustainable Mobility, our objective is to reveal and understand the system-level collective dynamics emerging in our rapidly developing mobility landscape. We combine empirical data analysis and theoretical complex systems modeling with tools from statistical physics and nonlinear dynamics to investigate diverse topics. Which collective phenomena emerge in shared and networked mobility? How should we develop and design infrastructure and services to promote sustainable mobility? How can we integrate innovative sustainable mobility services with existing public transport?

 

Ultimately, we strive to provide mechanistic insights and analysis tools to contribute to the design and implementation of more effective, fair, and sustainable mobility solutions.

 

Find out more about our research below!

 

Dynamics of Energy Systems, Climate and Sustainability

Energy fundamentally underlies all aspects of life. Its sustainable generation and reliable distribution are indispensable. We are currently transitioning from a fossil fuel-based energy system to one dominated by renewable sources. In addition, the same transition in other sectors, such as towards electric mobility, also affects the consumption of electricity. These drastic changes present an extraordinary challenge for the design and robust operation of future power grids and energy systems in general.

Our research focuses on principles of self-organization in high-dimensional dynamical systems to identify critical features of future-compliant energy systems. We build on fundamental aspects of dynamical systems theory, network dynamics, and time series analysis to characterize the dynamics of power grids and energy systems. This understanding enables us to predict and ultimately control instabilities emerging from the collective dynamics during the ongoing transition.

Our goal is to develop a fundamental understanding to support the integration of renewable energies and the transition to sustainable energy systems and usage.

Find out more about our research below!

Network Dynamics of Natural Computation

Biological systems in general, and neuronal circuits in particular, exhibit collective dynamics that is self-organized, distributed, decentrally controlled, and often microscopically unreliable. Yet, they typically exhibit robust and predictable functions and can reliably solve a variety of computational tasks.

Spatially and temporally coordinated patterns of neural activity are key to information processing in the brain. Yet, their dynamical origin and, more generally, the mechanisms underlying how distributed activity yields nonlinear computations in the brain are far from fully understood.

We study how heterogeneous interaction networks, non-additive coupling, and other nonlinearities act together to induce specific coordinated activity patterns and how neural and bio-inspired dynamical systems coordinate activity and thus effectively process, route, and transmit information.