# 2021/22 Taught Postgraduate Module Catalogue

## COMP5930M Scientific Computation

### 15 creditsClass Size: 150

Module manager: Dr Toni Lassila
Email: T.Lassila@leeds.ac.uk

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.

### Objectives

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.

### Syllabus

- 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

Due to COVID-19, teaching and assessment activities are being kept under review - see module enrolment pages for information

 Delivery type Number Length hours Student hours Lectures 22 1.00 22.00 Class tests, exams and assessment 1 2.00 2.00 Tutorial 11 1.00 11.00 Private study hours 126.00 Total Contact hours 35.00 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

Due to COVID-19, teaching and assessment activities are being kept under review - see module enrolment pages for information

Coursework
 Assessment type Notes % of formal assessment In-course MCQ MCQ/Short answer test 60.00 In-course Assessment Coursework - MATLAB exercises based on lectures 1-12 20.00 In-course Assessment Coursework - MATLAB exercises based on lectures 13-20 20.00 Total percentage (Assessment Coursework) 100.00

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