# 2020/21 Taught Postgraduate Module Catalogue

## COMP5930M Scientific Computation

### 15 creditsClass Size: 100

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 2020/21

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 Tutorial 11 1.00 11.00 Private study hours 117.00 Total Contact hours 33.00 Total hours (100hr per 10 credits) 150.00

### Private study

Taught session prep: 20 hours
Taught session follow-up: 40 hours
Self-directed study: 24 hours
Assessment activities: 33 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 Online Assessment Online Time Limited Assessment - 48hr 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