CO923 Computational Methods for Complex Systems
This programme is no longer running.
This is an option for the CDT in Mathematics of Real-World Systems taught programme.
Taken by students from:
|Code||Degree Title||Year of study||core or option||credits|
|P-G1PG||MSc in Mathematics of Systems||
|P-G1PF||Mathematics of Systems MSc+PhD||
|P-F3P6/7||Erasmus Mundus Masters in Complex Systems||
The module covers fundamental concepts of simulation of complex systems and numerical solution of related mathematical problems. It introduces several paradigms in the study of complexity, along with the methods used for their investigation and an overview of current research.
Basic numerical methods: systems of linear equations, solution of eigenvalue problems, matrix problems.
Introduction to Monte Carlo methods: ergodicity, detailed balance, the Ising model, uncertainty estimation.
Complex systems: emergence, self-organization, computational techniques.
Paradigms in complexity: cellular automata, percolation, self-organised criticality; applications to sociophysics, ecology and neuroscience.
Generation of complex networks: models, theorems, Monte Carlo algorithms.
Stability of networked dynamical systems.
PLEASE NOTE: This is NOT a programming module. Computer programming will NOT form a part of the material taught in class or assessed during the oral exam. Rather, this is a module about methods and algorithms. As such, it is assumed that all the students will have familiarity with some programming language and/or environment, which they will have to use for the coursework, which is computational in nature. The particular choice of the programming language (MATLAB, FORTRAN, C, Mathematica, etc.) is left entirely to the student and is completely irrelevant for the purposes of the module. Of course, the chosen programming language should still be fast enough to complete the assignments in time. The coursework will not be assessed based on the details of the code, but rather on the correctness of the results and on a short report explaining in detail the choices made for the implementation and their reasons.
W. H. Press et al., “Numerical recipes in C”, Cambridge University Press.
M. E. J. Newman and G. T. Barkema, “Monte Carlo methods in statistical physics”, Oxford University Press.
K. Huang, "Introduction to Statistical Physics", CRC Press.
P. L. Krapivsky, S Redner & E Ben-Naim, "A Kinetic View of Statistical Physics", Cambridge University Press.
J. H. Holland, "Complexity: A Very Short Introduction", Oxford University Press.
S. H. Strogatz, "Nonlinear Dynamics and Chaos", Westview Press.
Lectures per week
2 x 2 hours
Total contact hours
Private study and group working
Place and time:
Mondays: 2-4pm in D1.07
Tuesdays: 3-5pm in B1.01Viva_timetable_2Link opens in a new window
Assessment information 2016/ 2017:
There will be two computational problems set during the course, each worth 25% of the final mark based on written reports of the results. The deadlines for these will be 1 February and 7 March.
In week 10 there will be an oral examination which will be worth 50% of the final mark.
Suggested papers as basis for Assignment 2: ListLink opens in a new window
Timetable for Assignment 2 presentations: TimetableLink opens in a new window
Viva date: 14 March 2017
Tentative viva scheduleLink opens in a new window - updated!
Viva topicsLink opens in a new window
Slides from past lectures:
Introduction to ComplexityLink opens in a new window
Cellular automataLink opens in a new window
Statistical Physics and Monte Carlo methodsLink opens in a new window
Criticality and the Ising modelLink opens in a new window
Self-organised criticalityLink opens in a new window
Introduction to complex networksLink opens in a new window
Neuroscience and the Hopfield modelLink opens in a new window
Neural network dynamicsLink opens in a new window
SociophysicsLink opens in a new window
CooperationLink opens in a new window
Evolution and genetic algorithmsLink opens in a new window
Stochastic optimizationLink opens in a new window
C elegans networks:
C elegans neural networkLink opens in a new window
C elegans metabolic networkLink opens in a new window
Gene regulatory networks:
Human (healthy)Link opens in a new window
Human (cancer)Link opens in a new window
TroyLink opens in a new window