Scientific Computing III

5 credits

Syllabus, Master's level, 1TD397

A revised version of the syllabus is available.
Education cycle
Second cycle
Main field(s) of study and in-depth level
Computational Science A1N, Computer Science A1N, Technology A1N
Grading system
Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
Finalised by
The Faculty Board of Science and Technology, 3 June 2014
Responsible department
Department of Information Technology

Entry requirements

BSc degree where Scientific Computing II or Scientific computing, bridging course is included. Vector Calculus and linear algebra.

Learning outcomes

To pass, the student should be able to

  • explain the idea behind the algorithms that are considered in the course;
  • account for the fundamental difference between methods based on finite differences and finite elements and their advantages and disadvantages given different application problem;
  • interpret and relate computational results to the concepts consistency, stability and convergence;
  • solve problems in science and engineering given a mathematical model, by structuring the problem, choose appropriate numerical method and use advanced software and self-written code to generate solution;
  • use advanced computational software and interpret results from the computations;
  • present, explain, summarise, evaluate and discuss solution methods and results and formulate conclusions in a written report


The main focus is on solutions to partial differential equations and methods for solving the resulting equation system. Solution methods based on finite differences and finite element methods. Iterative methods for solutions to linear systems of equations. The Power method for eigenvalue problems. Theoretical, practical, implementational as well as validation aspects are discussed in relation to the methods presented in the course. Use of computational software (Comsol Multiphysics and MATLAB).

Examples of key concepts covered in the course: accuracy and order of accuracy, efficiency, consistency, stability, convergence.


Lectures, problem classes/workouts, laboratory work, compulsory assignments.


Written examination (3 credits) and approved assignments (2 credits). The assignments are reported in English.