Inferring the high dimension network structure in a multitude of coupled oscillators
Early Stage Researcher: Gloria Cecchini
Principle Investigators: Björn Schelter (Aberdeen, UABDN – major institution) and Michael Rosenblum (Potsdam, UP – partner institution)
Complex systems are typically represented by networks. Modern societies rely on the understanding of the dynamics of these networks; this understanding promises to disclose underlying mechanisms and characteristics of a multitude of systems. Examples include power grids, traffic networks, disease spreading, and terrorist networks. But also in various disciplines such as Physics, Biology, Neuroscience, Finance, Engineering or Medicine, networks are ubiquitous. Here, examples include climate networks from El Nino or Southern Oscillation and the Monsoon, stock market interactions, and biological networks, such as neuronal oscillators and brain activity measured from, e.g., electroencephalograms
The project is devoted to the development of novel cutting-edge techniques that enable inferring the network structure based on the potentially incomplete and noisy observation of the nodes’ dynamics. The project will particularly focus on oscillatory signals with the aim to distinguish direct and indirect interactions in large networks with potentially time-dependent topology. The approaches and frameworks developed will be compared to existing techniques. Rigorous mathematical derivations and the development of the corresponding statistics to judge the relevance of the findings complement this project.