Complexity Science - Einzelansicht

Course content:
Philosophical foundations of complexity / complexity science
Complex networks (mathematical basics, clustering, community detection, application to social networks)
Information theory (Shannon entropy and derived quantities, estimation techniques, application to time series analysis)
Black swans (rank-ordering and extreme value statistics, percolation and criticality, applications in climate science)
Selected topics

The course consists of lectures, discussion sessions, Jupyter notebook sessions, and mini projects. The course is targeted toward physics and mathematics (with some statistical-physics knowledge) MSc and PhD students.

What is a complex system, Yale University Press, 2020

Critical phenomena in natural sciences: chaos, fractals, selforganization and disorder: concepts and tools, Springer (2006)

MacKay, David JC. Information theory, inference and learning algorithms. Cambridge university press (2003)

Newman, Mark. Networks. Oxford university press, 2018.

Selected research papers

The course will be held in English.

Basic knowledge in statistical physics

Lectures on theoretical background in complexity science: network theory, information theory, basic statistics and statistical physics;

Discussions of classic papers in complexity science;

Presentations of examples of recent scientific publications in complexity science;

Tutorials with practical programming tasks using Jupyter notebooks;

Discussions on foundational issues in complexity science.