Scientific Computing II
Syllabus, Bachelor's level, 1TD395
- 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
Scientific Computing I. Mathematical Statistics is recommended.
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).