Scientific Computing II

5 credits

Syllabus, Bachelor's level, 1TD395

A revised version of the syllabus is available.
Education cycle
First cycle
Main field(s) of study and in-depth level
Computer Science G1F, Technology G1F
Grading system
Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
Finalised by
The Faculty Board of Science and Technology, 14 May 2013
Responsible department
Department of Information Technology

Entry requirements

Scientific Computing I. Mathematical Statistics is recommended.

Learning outcomes

To pass, the student should be able to

  • describe and perform tasks in connection to the key concepts covered in the course;
  • explain the idea behind and apply the algorithms covered in the course;
  • explore properties for numerical methods and mathematical models by using the analysis methods covered in the course;
  • evaluate algorithms and mathematical models and discuss the suitability of these methods on a given problem;
  • based on such evaluation, discuss the suitability of methods given different different applications;
  • given a mathematical model, solve problems in science and engineering by structuring the problem, choose appropriate numerical method and generate solution using software and by writing programming code;
  • present, explain, summarise, evaluate and discuss solution methods and results in a report.


Continued programming in MATLAB. Continued problem solving methodology. Data analysis: least squares problems with solution based on the normal equations. Interpolation and piecewise interpolation (including cubic splines). Solution to ordinary differential equations (initial-value problem). Adaptivity. Stability. Explicit and implicit methods. Monte Carlo methods and methods based on random number, stochastic models, stochastic simulation, inverse transform sampling.

Key concepts covered in the course: discretisation and discretisation error (truncation error), accuracy and order of accuracy, the local and global error, efficiency, stability, adaptivity, stiff and non-stiff ordinary differential equations, deterministic/stochastic models and methods, ansatz, interpolation, least squares.


Lectures, problem classes, laboratory work, mini projects.


Written examination (3 credits) and approved mini projects (2 credits).