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ACM40080

Academic Year 2022/2023

Advanced Topics in Computational Science (ACM40080)

Subject:
Applied & Computational Maths
College:
Science
School:
Mathematics & Statistics
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Assoc Professor Conor Sweeney
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module will introduce the student to the techniques of advanced computational science with a focus on applying those methods to applied problems taken from the physical sciences.

The course will begin with a review of Partial Differential Equations, and computational solutions of the wave equation, the diffusion equation and Poisson's equation.

The course will also consider other computational approaches to solving governing equations for physical systems, such as the Finite Element Method and Finite Volume Method.

Uncertainty in computational models will be discussed, with reference to model ensembles.

Using computer servers for large computational work will be discussed, and students will be introduced to using a terminal and essential linux commands. Students will have the opportunity to run code on a computer cluster.

All students should have prior knowledge of Partial Differential Equations, and must have their own laptop and prior experience of coding in python.

About this Module

Learning Outcomes:

On successful completion of the course, the student will be able to:

1) Analyse the stability and convergence of discretised Partial Differential Equations.
2) Discuss the strengths and weaknesses of different numerical techniques used to solve Partial Differential Equations.
3) Use linux commands on a terminal to run code on a remote server.
4) Use python code to analyse and plot data produced by physical models.

Indicative Module Content:

Discretisation of PDEs.
Finite difference method
Finite Element Method.
Uncertainty in models.
Use of the terminal and linux commands.

Student Effort Hours:
Student Effort Type Hours
Lectures

24
Computer Aided Lab

12
Specified Learning Activities

32
Autonomous Student Learning

60
Total

128

Approaches to Teaching and Learning:
Lectures and tutorials.

Requirements, Exclusions and Recommendations
Learning Requirements:

Students must be familiar with partial differential equations.

Student must be able to use python to solve equations, calculate data and produce plots.


Module Requisites and Incompatibles
Additional Information:
Students must have successfully completed a module on Partial Differential Equations. Students must have existing Python coding skills.

Equivalents:
Advanced Computational Science (MAPH40080)


 

Assessment Strategy
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Class Test: Two in-class written tests Varies over the Trimester n/a Standard conversion grade scale 40% No
50
 No
Continuous Assessment: Online quizzes Varies over the Trimester n/a Standard conversion grade scale 40% No
30
 No
Project: Individual video project Varies over the Trimester n/a Standard conversion grade scale 40% No
20
 No

Carry forward of passed components
No
 

Resit In Terminal Exam
Autumn Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Group/class feedback, post-assessment
• Online automated feedback

How will my Feedback be Delivered?

Not yet recorded.

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