Workshop I

Data-driven modeling and analysis

April 26, 2022

The goal of this workshop is to provide an overview of data-driven methods for the analysis of time-series data. Given only measurement or simulation data, these methods can be used to extract global properties of the underlying system such as time scales and metastable sets or to directly learn the governing equations of the system. Other topics of interest include multiscale modeling, homogenization, dimensionality reduction, manifold learning, and control as well as applications in molecular dynamics, fluid dynamics, climate science, and engineering.


The workshop is part of the Thematic Einstein Semester “The Mathematics of Complex Social Systems: Past, Present, and Future”.

Invited Speakers

Nelida Črnjarić-Žic (University of Rijeka)

Alexandre Mauroy (University of Namur)

Marina Meila (University of Washington)

Antonio Navarra (Euro-Mediterranean Center on Climate Change, University of Bologna)

Sebastian Peitz (University of Paderborn)

Konstantinos Zygalakis (University of Edinburgh)

Organizational detail

The workshop takes place as a hybrid event, where the on-site part will be at Zuse Institute Berlin in Berlin-Dahlem.


Speaker Title
9:00-9:45 Alexandre Mauroy Stability analysis in Koopman operator theory: A data-driven approach
9:45-10:30 Nelida Črnjarić-Žic The Koopman operator-based data-driven algorithms on nonautonomous and stochastic dynamics
10:30-11:00 Coffee break  
11:00-11:45 Sebastian Peitz Efficient data-driven prediction and control of complex systems via the Koopman operator
11:45-12:30 Antonio Navarra Koopman modes of the sea surface temperature in the Tropical Pacific
12:30-14:00 Lunch break  
14:00-14:45 Konstantinos Zygalakis Hybrid modeling for the stochastic simulation of spatial and non-spatial multi-scale chemical kinetics
14:45-15:15 Luzie Helfmann Collective variables and tipping analysis of agent-based models
15:15-15:45 Coffee break  
15:45-16:15 Kateryna Melnyk Understanding network dynamics with graph representation learning
16:15-16:45 Ming Zhong Machine learning of self organization from observation
16:45-17:00 Short break  
17:00-17:45 Marina Meila Manifold Learning 2.0: Explanations and eigenflows


Registration is free of charge. To register for this workshop please use the link:

Deadline for registration: April 16, 2022

MATH+ organizers

Stefan Klus (U Surrey), Stefanie Winkelmann (ZIB)