ACM30140 Numerical Methods for PDEs

Academic Year 2024/2025

This module introduces the methods and underlying theory used in the numerical solution of partial differential equations (PDEs). The finite difference method will be used to find solutions to the heat equation (parabolic PDE), wave equation (hyperbolic PDE) and Laplace equation (elliptic PDE). Stability and convergence issues will be discussed. The module will end with a brief overview of the finite element method and finite volume method for finding approximate solutions to PDEs.

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Curricular information is subject to change

Learning Outcomes:

On completion of this module, students should be able to:
- Apply the finite difference method to solve PDEs
- Ensure that PDEs are well-posed
- Ensure that FDE solution is stable
- Apply the finite element method to solve PDEs

Student Effort Hours: 
Student Effort Type Hours
Lectures

24
Tutorial

12
Autonomous Student Learning

72
Total

108
Approaches to Teaching and Learning:
Lectures and tutorials. 
Requirements, Exclusions and Recommendations
Learning Requirements:

The prerequisites for ACM30140 are:
- a course in PDEs
- knowledge of coding in Python with Jupyter notebook
- knowledge of linux commands, and using the terminal to connect to servers.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Exam (In-person): Class test n/a Alternative linear conversion grade scale 40% No

20
Assignment(Including Essay): 6 Brightspace quizzes n/a Alternative linear conversion grade scale 40% No

30
Exam (In-person): End Of Semester Exam n/a Alternative linear conversion grade scale 40% No

50

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

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback

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