Entry requirements

Scientific Computing I. Mathematical Statistics is recommended.

Learning outcomes

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 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.

Content

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.

Instruction

Lectures, problem classes, laboratory work, mini projects.

Assessment

Written examination (3 credits) and written report on mini project (2 credits).