Scientific Computing

Course content

This course focuses on the general techniques and ideas found in professionally written numerical software, as well as the general concepts one needs to know for applying suitable software in a qualified manner to computational problems. Thus, the course is aimed much more at potential users of mathematical software than at potential creators of such software.


MSc Programme in Nanoscience

MSc Programme in Physics

MSc Programme in Physics with minor subject

Learning outcome

At course completion, the student should be able to:

  • Choose an appropriate numerical method for the solution of the problem or sub-problem. The numerical method is selected among the methods presented in the course and it should be chosen with respect to the requirements of the model.
  • Evaluate the numerical method with respect to potential accuracy, computational efficiency, robustness and memory requirements.
  • Perform the required computation using Matlab or similar systems.
  • Evaluate the quality of the solution with respect to the accuracy obtained and the sensitivity to model parameter variations.
  • Estimate whether the quality of the solution is adequate relative to the desired use of the model.
  • Analyse the reasons of a possible total failure of a method applied to a concrete problem.



The student will be able to use the methods presented in the course to perform numerical analysis of simple mathematical models from science in order to solve concrete problems and to evaluate the results obtained. The solution will mainly be based on Matlab or similar systems.



The student will know about

  • simple mathematical models from science and numerical analysis of them.
  • ideas behind and motivation for fundamental numerical methods for the solution of: linear and nonlinear equations, linear and nonlinear optimization, eigenvalue problems, initial value problems for ordinary differential equations, partial differential equations and the fast Fourier transform.

Lectures, exercises and 4 small projects. Duration 9 weeks.

See Absalon for final course material. The following is an example of expected course litterature.

Michael T. Heath: Scientific computing. An introductory survey, from McGraw-Hill.

It is recommended that the student has knowledge of mathematics corresponding to the contents of the B.Sc. in physics, mathematics or computer science.
The course assumes programming experience in a language like Matlab, Python (NumPy) or C++

Academic qualifications equivalent to a BSc degree is recommended.

It is expected that the student brings a laptop
Necessary software:
Windows: Xming
Xming:http:/​/​​projects/​xming/​ & http:/​/​​XmingNotes/​
For support please contact SCIENCE IT, e-mail:, 35 32 21 00
Linux:X11 runs automatically
MAC: For all systems since OS 10.5 you can use X11, which you can download for free at http:/​/​​landing/​.
X11 is a part of OS X in Leopard and Lion.

Continuous feedback during the course of the semester
7,5 ECTS
Type of assessment
Continuous assessment, during course
Independent evaluation of 4 projects. The final grade is the average of the grades of each of the 4 projects.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
More internal examiners
Criteria for exam assessment

See learning outcome

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 32
  • Preparation
  • 58
  • Practical exercises
  • 16
  • Project work
  • 100
  • English
  • 206