Complex Physics

Course content

The topics that will be covered are: Phase transitions, Ising model, critical phenomena, Monte Carlo simulations, Percolation, Networks, interfaces, agent-based models, self organization, scale free phenomena, game theory, econophysics and models of social systems.


MSc Programme in Physics

MSc Programme in Physics w. minor subject

Learning outcome


The aim is to learn how to rephrase a complex phenomenon into a mathematical equation or computer algorithm. At the conclusion of the course students will be able to implement and analyze simple quantitative models on a computer. Students will learn how to appreciate that the joint dynamics of a many body system often is qualitatively different from the simple sum of its parts.



The student is expected to gain basic knowledge on contemporary research in complex systems. This includes the ability to use fundamental concepts from statistical mechanics, non-linear dynamics, time series analysis, agent based models and self-organizing systems. This includes the concepts of scaling and scale-invariant phenomena, e.g. fractals or scale-free networks. 



How to describe and analyze non-linear systems and systems with many components in terms of equations and algorithms.

Write computer models of systems with many interacting parts, including Monte-Carlo simulations, interfaces, networks, and cellular automata.

Implement agent based models to describe self-organized dynamics of structures, for example within network theory and systems that behave similar across a wide range of scales.


The course will provide the students with tools from physics that have application in a range of fields within and beyond physics. 

lectures and exercises

lecture notes

Students would gain by having taken a course on Dynamical Systems and Chaos, as well as by having some knowledge of statistical mechanics and knowledge of some programming language.
These requirement are however non-mandatory, and with some effort the course can be followed by other students, in particular students with background in mathematics, bio-informatics, economics or computer sciences.

The course also suited for physics students who plan to write their thesis within complex systems, biological physics or quantitative models of biological systems.
Students from mathematics, bio-informatics, economics and computer science are welcome.

7,5 ECTS
Type of assessment
Oral examination, 30 minutes
Oral exam without preparation time
Without aids
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Criteria for exam assessment

see learning outcome

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 24
  • Theory exercises
  • 28
  • E-Learning
  • 2,5
  • Exam
  • 0,5
  • Preparation
  • 151
  • English
  • 206,0