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Mastering Complexity

To understand the complex processes in the world around us and to interpret the vast amount of complex datasets available today  we need a set  of scientific methods. This website links to courses that will provide the basic tools and concepts to model and quantitatively analyze complex systems or data sets and will enable the students to apply this knowledge to their specific scientific field or in an interdisciplinary context.



Modeling complex dynamical systems

To understand the dynamics of complex systems it is not enough to understand their parts. It is important to understand the interactions and the new phenomena resulting from the interplay between the pieces. Such systems can be found everywhere in natural and social sciences or in technology. Examples are the climate system, interacting species, the dynamics of economic systems or the power grid to name just a few.

Einführung in die nichtlineare Physik

Back of the envelope science

Praktikum nichtlineare Modellierung

Tutorials on continuation

Data driven approaches


Bayesian statistics

Machine learning and artificial intelligence

Causal inference

Introduction to nonlinear dynamics and self-organization  Next Date: WT 20/21

Introduction to Bayesian statistics  Next Date: WT 20/21

Introduction to machine learning  Next Date: ST 20

A short Course on Causal Inference  Next Date: ST 20

Fermi questions and orders of magnitude -- Science on the back of the envelope  Next Date: tba



Interdisciplinary lectures on nonlinear complex systems

Here you can find some interdisciplinary lectures on topics important for modeling and analyzing complex systems.

Interdisciplinary lectures

Hands-on training nonlinear modeling in natural sciences

In this workshop, students from mathematics, physics, chemistry and biology work together in small groups for one semester on a project.

Hands-on training summer term 2020

Münsteranian Torturials

Here you can find some tutorial manly on continuation

Münsteranian Torturials