MODELLING AND NUMERICAL SIMULATIONS
Learning outcomes of the course unit
The course aims at providing an elementary introduction to modeling and numerical simulations techniques, which are of common usage in Computational Physics. Though born in the framework of scientific applications, these techniques are as a matter of fact of general relevance. As a results, they proved to be effective in a variety of fields (economics and finance, computer networks, computational biophysics). In view of this, the course will be to a large extent a collection of topics presented in a seminar style. On top of providing conceptual and technical tools, the course will aim in the end at tackling a project, in which the students will finalize one of the simulations introduced during the classes. This activity will be the subject of the final examination.
Course contents summary
As a starting point, we will recall basics of probability and statistics, with an emphasis on numerical techniques (probability functions generation, data analysis). Data analysis will give the chance to introduce modeling in the simple form of data fitting. A large fraction of the course will be devoted to applications of Markov processes theory. Modeling of queues will be introduced; simple examples of dynamic MonteCarlo will also be tackled. An elementary introduction to stochastic differential equations will be provided, in particular facing Langevin equation (brownian motion and tree-cutting problem). Basics of percolation theory will be introduced as an example of how a simple model can model a variety of phenomena.
Notes provided by the lecturer.
Style will be mostly informal, giving emphasis to problem solving. In view of this, every subject will be tackled also via numerical experiments.
Assessment methods and criteria
Evaluation will be in part in itinere, via the assignement of numerical exercises. At the end every student will be assigned a problem to be solved via numerical simulations. Students will present their solution toghether with a report.