Syllabus for Scientific Computing II
- 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:
- Revised: 2019-02-19
- Revised by: The Faculty Board of Science and Technology
- Applies from: Autumn 2019
Scientific Computing I. Mathematical Statistics is recommended.
- Responsible department: Department of Information Technology
Upon completion of the course, 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 the properties in algorithms and mathematical models and, based on the evaluation discuss the suitability of these methods given an application problem;
- solve problems in science and engineering by breaking the problem into sub-problems, use software efficiently and write code using a good programming standard;
- present, explain, summarise, evaluate and discuss solution methods and results in a report.
Programming in MATLAB and methodology for problem solving. Solution to ordinary differential equations (initial-value problem). Adaptivity. Stability. Explicit and implicit methods. Order of a method, order of accuracy. Monte Carlo methods and methods based on random number, stochastic models, stochastic simulation, stochastoc ordinary differential equations, inverse transform sampling.
Key concepts covered in the course: discretisation and discretisation error (truncation error), accuracy and order of accuracy, local and global error, efficiency, stability and instabilty, adaptivity, stiff and non-stiff ordinary differential equations, deterministic/stochastic models and methods.
Lectures, problem classes, laboratory work, mini projects.
Written examination (3 credits). Written report on mini project and problem sovling tasks (2 credits).
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.
- Latest syllabus (applies from Autumn 2019)
- Previous syllabus (applies from Spring 2018)
- Previous syllabus (applies from Spring 2013)
- Previous syllabus (applies from Autumn 2011)
- Previous syllabus (applies from Autumn 2010)
- Previous syllabus (applies from Autumn 2009)
- Previous syllabus (applies from Autumn 2007)
Applies from: Autumn 2019
Some titles may be available electronically through the University library.
Chapra, Steven C.
Applied numerical methods with MATLAB for engineers and scientists
3. international ed.: Boston: McGraw-Hill Higher Education, 2012