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
Machine learning and artificial intelligence
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
Interdisciplinary lectures on nonlinear complex systems
Here you can find some interdisciplinary lectures on topics important for modeling and analyzing complex systems.
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.
Here you can find some tutorial manly on continuation