Dynamical Methods in Data-based Exploration of Complex Systems

International Workshop
07 - 11 October 2019

Understanding underlying complex nonlinear dynamical processes from
observations is a challenging problem even in the era of Big Data.

Recently, novel approaches have been developed at the overlap of
dynamically based techniques and methods from machine learning and data
assimilation. At the workshop, general advanced tools of data-based
understanding of complex systems and their particular applications will be discussed.

 

 

Topics include

  • machine learning of dynamical systems
  • reservoir computing
  • Koopman operator approach
  • compressive sensing
  • data assimilation
  • nonlinear time series analysis of spatiotemporal data
  • network reconstruction
  • coupling function inference
  • nonstationarity and hidden variables
  • applications in physiology, neuroscience, social networks, power grids, climate, etc.

Invited speakers

H. Abarbanel (US)
R. Andrzejak (ES)
J. Bröcker (UK)
S. Daun (DE)
C. Grebogi (UK)
P. Ivanov (US)
J. Kurths (DE)
Y.-C. Lai (US)
K. Lehnertz (DE)
C. Letellier (FR)
Z. Levnajic (SI)
C. Masoller (ES)
A. Mauroy (BE)
E. Ott (US)
J. Peinke (DE)
M. Rosenblum (DE)
T. Sauer (US)
B. Schelter (UK)
I. Sendiña-Nadal (ES)
A. Stefanovska (UK)
M. Timme (DE)
J. Timmer (DE)
P. van Leeuwen (UK)
A. Witt (DE)

 

 

Scientific Coordinators

Holger Kantz
(Max Planck Institute for the Physics of Complex Systems, Germany)

Ulrich Parlitz
(Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany)

Arkady Pikovsky
(University of Potsdam, Germany)

Organisation

Mandy Lochar
(Max Planck Institute for the Physics of Complex Systems, Dresden, Germany)

Application

The call for applications is closed.

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Scientific Program

The scientific program of the workshop can be found here.

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