Course content

Linear algebra, finite difference methods, stochastic methods, ordinary differential equations, partial differential equations, optimization, linear programming, genetic algorithms, simulated annealing, Fourier methods, wavelet analysis, Monte Carlo methods, molecular dynamics, quantum mechanics, cellular automata.

Learning outcome

Knowledge:

The student will learn a selection of computational methods and how these can be used to solve problems within different fields of physics.

Skills and general competence:

The student will be able to formulate a strategy to solve a given problem using one or more computational methods, make it concrete through programming and running the program and then critically analyze the obtained result, communicate the result in writing in a format close to the standard used in international physics journals.

Learning methods and activities

Lectures and exercises. The course will be given in English if students on the international master program in physics are attending the course. Expected workload in the course is 225 hours.

Compulsory assignments

• Exercises

Recommended previous knowledge

Basic knowledge of physics. It is expected that the student has basic skills in programming, e.g. at the level of the course TDT4102.

Course materials

Lecture notes in English; Press, Flanery, Teukolsky and Vetterling: Numerical Recipes.

Credit reductions