Syllabus for Scientific Computing II
A revised version of the syllabus is available.
- 5 credits
- Course code: 1TD395
- Education cycle: First cycle
Main field(s) of study and in-depth level:
Computer Science G1F,
Explanation of codes
The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:
- G1N: has only upper-secondary level entry requirements
- G1F: has less than 60 credits in first-cycle course/s as entry requirements
- G1E: contains specially designed degree project for Higher Education Diploma
- G2F: has at least 60 credits in first-cycle course/s as entry requirements
- G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
- GXX: in-depth level of the course cannot be classified
- A1N: has only first-cycle course/s as entry requirements
- A1F: has second-cycle course/s as entry requirements
- A1E: contains degree project for Master of Arts/Master of Science (60 credits)
- A2E: contains degree project for Master of Arts/Master of Science (120 credits)
- AXX: in-depth level of the course cannot be classified
- Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Established: 2007-03-15
- Established by: The Faculty Board of Science and Technology
- Revised: 2009-05-12
- Revised by: The Faculty Board of Science and Technology
- Applies from: Autumn 2009
Scientific computing I. Mathematical Statistics is recommended.
- Responsible department: Department of Information Technology
To pass, the student should be able to
- describe the fundamental concepts discretisation, accuracy and order of accuracy, efficiency, stability, discretisation errors (truncation error), ansatz, adaptivity;
- in general terms explain the idea behind the algorithms that are presented in the course;
- describe the fundamental difference between stochastic and deterministic methods and models;
- analyse the order of accuracy and stability properties for basic numerical methods and understand how such an analysis is employed;
- evaluate methods with respect to accuracy, stability properties and efficiency;
- 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 short report.
Continued programming in MATLAB. Continued problem solving methodology. Data analysis: least squares problems with solution based on the normal equations. Interpolation with an emphasis on piecewise interpolation (including cubic spines). Solution to ordinary differential equations (initial-value problem). Adaptivity. Stability. Explicit and implicit methods and in connection with this solution to non-linear equation systems. Monte Carlometoder and methods based on random number, stochastic models, stochastic simulation, inverse transform sampling.
Lectures, problem classes, laboratory work, compulsory assignments.
Written examination at the end of the course and compulsory assignments/mini projects.
- Latest syllabus (applies from Autumn 2019)
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- Previous syllabus (applies from Autumn 2010)
- Previous syllabus (applies from Autumn 2009)
- Previous syllabus (applies from Autumn 2007)
Applies from: Autumn 2009
Some titles may be available electronically through the University library.
Chapra, Steven C.
Applied numerical methods with MATLAB for engineers and scientists
2nd. ed.: Boston: McGraw-Hill Higher Education, 2008