Scientific Model Building
The aim of this course is to introduce students to the field of scientific modelling, model synthesis and analysis. As such, less emphasis will be on a "top-down" approach than in standard lecture courses. Students will have an opportunity to decide which topics will shape the direction of the course.
Topics to be covered:
- History of Complex Systems (Turing, von Neumann, Ulam, Conway, Wolfram)
- Introduction to Cellular Automata (1D/2D CA, rule codes,phenomenological studies, behaviour classes)
- CA Models of Fluid Dynamics (Lattice Gas Automata)
- Self-Organized Criticality (Sandpile Model, Forest fire model)
- Complex systems and emergence in Complex Systems
- Complex Networks, Small World Networks
One or two other topics will also be covered depending on the interests of students. This could cover topics including (but not restricted to): CA based Neural Networks, Dynamical Systems, Self-reproducing, Generalized CA models (probabilistic, reversible, quantum, structurally dynamic, etc.), Applications of CA.
Evaluation of outcomes
Learning requirements, mode of evaluation, criteria of evaluation:
mode of evaluation: examination
- Boccara, N. (2004). Modeling complex systems. New York: Springer. (http://www.amazon.com/gp/product/0387404627/103-6032108-4445446?v=glance&n=283155 )
- Newman, M. E. J. (2010). Networks: an introduction. Oxford ; New York: Oxford University Press. (http://www.amazon.com/Networks-Introduction-Mark-Newman/dp/0199206651 )
- Background material: Ilachinski, A. (2001). Cellular Automata:A Discrete Universe. Singapore: World Scientific Publishing Co. Pte. Ltd.