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Content and learning outcomes
The course will give you knowledge about advanced computer methods based on numerical algorithms for solving mathematical models from scientific and engineering applications, in particular about how to formulate, analyze and implement them. More specifically, the course includes:
- numerical treatment of ordinary differential equations,
- finite difference methods and basic finite element methods for, mainly linear, partial differential equations,
- numerical solution of linear systems of equations by direct and iterative methods,
- orientation about mathematical modeling.
Intended learning outcomes
For the mathematical models in the course contents (e.g. ordinary and partial differential equations, linear systems of equations) the student shall be able to:
- select suitable numerical algorithms,
- analyze numerical methods with respect to computational cost, accuracy and stability,
- apply and implement numerical algorithms in a suitable programming language,
- classify and characterize the mathematical models.
In addition, the student shall be able to:
- estimate the accuracy of numerical results,
- describe limitations of mathematical models and numerical methods,
- for a given numerical problem, present, discuss and summarize the problem, solution method and results in a clear way,
- work inteams to solve a numerical problem.
Literature and preparations
- English B / English 6
- Completed basic course in numerical analysis (SF1544, SF1545or equivalent)
- Completed basic course in differential equations (SF1633, SF1683or equivalent).
Completed basic courses in numerical analysis equivalent to SF1544 or similar, mathematical courses corresponding to linear algebra, calculus and differential equations, good handling with MATLAB.
Lennart Edsberg: Introduction to computation and modeling for differential equations, Wiley 2008, ISBN 978-0-470-27085-1
Lecture notes about numerical algebra.
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
- LABA - Laboratory Work, 4.5 credits, grading scale: A, B, C, D, E, FX, F
- TEN1 - Written Examination, 3.0 credits, grading scale: A, B, C, D, E, FX, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
Opportunity to complete the requirements via supplementary examination
Opportunity to raise an approved grade via renewed examination
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.Course web SF2520
Main field of study
SF2521 and other continued courses in Numerical methods