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2021/22 Taught Postgraduate Module Catalogue

COMP5930M Scientific Computation

15 creditsClass Size: 150

Module manager: Dr Toni Lassila

Taught: 1 Sep to 31 Jan (adv yr), Semester 1 (Sep to Jan) View Timetable

Year running 2021/22

This module is not approved as an Elective

Module summary

Understand the range of problems that can be formulated as nonlinear equation systems.Consider standard algorithms for these problems and the efficiency of their implementation.Demonstrate how state-of-the-art algorithms deliver gains in efficiency and allow the solution of large, sparse systems of nonlinear equations.


On completion of this module, students should be able to:
- understand the role of computational methods in Scientific Computing and the importance of reliability, efficiency and accuracy;
- demonstrate awareness of the state-of-the-art in Scientific Computing algorithms for the solution of nonlinear problems;
- understand the practical issues associated with implementation in code;
- demonstrate awareness of typical applications for such software.

Learning outcomes
On completion of the year/programme students should have provided evidence of being able to:
-to demonstrate in-depth, specialist knowledge and mastery of techniques relevant to the discipline and/or to demonstrate a sophisticated understanding of concepts, information and techniques at the forefront of the discipline;
-to exhibit mastery in the exercise of generic and subject-specific intellectual abilities;
-to demonstrate a comprehensive understanding of techniques applicable to their own research or advanced scholarship;
-proactively to formulate ideas and hypotheses and to develop, implement and execute plans by which to evaluate these;
-critically and creatively to evaluate current issues, research and advanced scholarship in the discipline.


- Numerical solution of a single nonlinear equation.
- Extension of the algorithms to systems of nonlinear equations and reduction to a series of linear equation systems.
- The concept of nonlinear partial differential equations and example applications.
- The need for reliable, efficient and accurate numerical approximation and how this results in discrete systems of nonlinear equations.
- Efficient direct and iterative solution algorithms for large, sparse, linear equation systems.
- Application to problems from classical fluid mechanics and other nonlinear partial differential equations.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Class tests, exams and assessment12.002.00
Private study hours126.00
Total Contact hours35.00
Total hours (100hr per 10 credits)161.00

Private study

Taught session prep: 22 hours
Taught session follow-up: 44 hours
Self-directed study: 25 hours
Assessment activities: 35 hours

Opportunities for Formative Feedback

Attendance and formative coursework.

Methods of assessment

Assessment typeNotes% of formal assessment
In-course MCQMCQ/Short answer test60.00
In-course AssessmentCoursework - MATLAB exercises based on lectures 1-1220.00
In-course AssessmentCoursework - MATLAB exercises based on lectures 13-2020.00
Total percentage (Assessment Coursework)100.00

This module will be reassessed by an online time-constrained assessment

Reading list

The reading list is available from the Library website

Last updated: 15/03/2022 16:12:19


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