Institute Seminar | Marc Timme | 2022-10-04 | 2/2 Discussion
Channel: CASUS.science
Date: 25/10/2022

TITLE: Network dynamics as an inverse problem - Detecting network structure and leaks from limited data by Marc Timme, Chair for Network Dynamics, Institute for Theoretical Physics and Center for Advancing Electronics Dresden (cfaed), TU Dresden

Marc received the degree in physics and mathematics in Würzburg, Stony Brook, USA, and Göttingen, Germany. After working as a Postdoctoral Researcher at the Max Planck Institute for Flow Research and as a Research Scholar at Cornell University, Ithaca, NY, USA, he was selected by the Max Planck Society to the Head of Broadly Cross-Disciplinary Max Planck Research Group on Network Dynamics, Max Planck Institute for Dynamics and Self-Organization. He held a Visiting Professorship at TU Darmstadt and was a Visiting Faculty at ETH Zürich. He is currently a Strategic Professor and the Head of the Chair for Network Dynamics at the Center for Advancing Electronics Dresden (cfaed), the Institute for Theoretical Physics, and the Cluster of Excellence Physics of Life, TU Dresden. Among other distinctions, Marc won the Otto Hahn Medal of the Max Planck Society and the Berliner-Ungewitter Award.

Marc is interested in building mathematical, conceptual and algorithmic foundations towards an understanding of the collective nonlinear dynamics of networks and complex systems. Applications fields include biological and bio-inspired technical systems, future-compliant mobility and energy systems, network economy & sustainability as well as network inverse problems of inference, design, and control.

ABSTRACT: The dynamics of natural and engineered networks enables the function of a variety of systems we rely on every day, from metabolic circuits in the cell and neural networks in the brain to electric power grids and water supply networks. To date, it remains unclear how to extract key features of networks if only time series data from (some) units are available. Marc reports on recent progress on detecting structural features from observed dynamics. First, he demonstrates how to identify the number N of dynamical variables making up a network – arguably its most fundamental property – from recorded time series of only a small subset of nN variables. Second, he sketches an approache to uncover network topological features from observed nodal time series data. Finally, Marc will present first steps towards identifying leak locations in (water) supply networks from observing flow changes at a limited number of sites in the network.

The work presented is based on contributions also from Jose Casadiego, Mor Nitzan, Hauke Haehne, Georg Boerner, Benjamin Sauer and others.

1. Topical Review: Marc Timme & Jose Casadiego,  J. Phys. A 47:343001 (2014).
2. Casadiego et al., Nature Comm. 8:2192 (2017).
3. Nitzan et al., Science Adv. 3:e1600396 (2017).
4. Haehne et al., Phys. Rev. Lett. 122:158301 (2019).
5. Boerner et al., in prep. (2022).
6. Sauer et al., in prep. (2022).

LICENSE:
CC BY-SA 4.0

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