
After completion of the course, the student

has gained insight into the main aspects of modeling and simulation in the context of statistical physics, such as universality, critical exponents, finitesize scaling, thermalization and error estimation

can write a simulation program for simple models in physics

can use such a simulation program to study the physics properties of these models; this includes a sensible choice for model parameters such as temperature, box size, and duration / number of iterations

is familiar with the basics of Monte Carlo methods

can report on computer simulations and the physics results obtained from the simulations in a scientific document


An important aspect of physics research is modeling: complex physical systems are simplified through a sequence of controlled approximations to a model that lends itself for computations, either analytic or by computer. In this course, the origin of a number of widely used models will be discussed. Magnetic systems as well as the liquidgas transition is modelled by the Ising model, polymers are often modelled by random walks, liquid flow is often modelled by lattice Boltzmann gases. Insight into these models can be obtained through a number of ways, one of which is computer simulation. During the course, simulation methods for these models will be discussed in the lectures as well as in computer lab sessions. Prerequisite: Elementary programming skills and some statistical physics.



 CompetenciesEntry requirementsPrerequisite knowledgeElementary programming skills and some statistical physics. 
  Required materialsSoftwareXming (beschikbaar in MyWorkPlace) 

 Recommended materialsBookM.E. J. Newman and G.T. Barkema, Monte Carlo methods in statistical physics, Oxford University Press. 
 BookD.Frenkel and B.Smit, Understanding Molecular Simulation: From Algorithms to Applications, Academic Press 

 Instructional formatsTestsFinal resultTest weight   100 
Minimum grade    


 