Inverse Network Dynamics - Network structure and function from nonlinear dynamics and time series data

Moved from 2020 to 2022 due to Covid-19.

International Seminar & Workshop
12 - 21 September 2022

In contrast to the forward procedure of analyzing complex systems, we consider inverse problems: Topical questions include how to infer structure and models from multi-dimensional time series and how to design networks for a desired function. Progress relies on the rapidly increasing availability of time series data and novel tools for analyzing large data sets. The event shall bridge theory and method development with applications from biology and physics to computer science and engineering.


Topics include

  • multi-dimensional time series and data analytics
  • structural vs. statistical inference
  • probabilistic models
  • causal inference
  • compressed sensing
  • networks in biology, biomedical and engineering applications
  • bio-engineering and synthetic biology
  • unreliable, subsampled and heterogeneous data
  • model reduction
  • network design
  • machine learning and dynamical systems
  • computational and algorithmic challenges

Invited speakers

B. Barzel (IL)
S. Bialonski (DE)
S. Boccaletti (IT)
J. Casadiego (ES)
E. Ching (HK)
C. Grebogi (UK)
S. Grün (DE)
H. Kantz (DE)
N. Karaiskos (DE)
B. Lünsmann (DE)
E. Ott (US)
U. Parlitz (DE)
A. Pikovsky (DE)
V. Priesemann (DE)
M. Rosenblum (DE)
J. Runge (DE)
B. Schelter (UK)
E. Schneidman (IL)
S. Shai (US)
L. Tupikina (FR)
C. Uhler (US)

Scientific Coordinators

Klaus Lehnertz
(University of Bonn, Germany)

Mor Nitzan
(Harvard University, Cambridge, USA)

Marc Timme
(Technical University of Dresden - Center for Advancing Electronics, Germany)


Maria Voigt
(Max Planck Institute for the Physics of Complex Systems, Dresden, Germany)

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Poster [pdf-file]

(original 2020 version)