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Curricular information is subject to change
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.
Discretisation of PDEs.
Finite difference method
Finite Element Method.
Uncertainty in models.
Use of the terminal and linux commands.
|Student Effort Type||Hours|
|Computer Aided Lab||
|Specified Learning Activities||
|Autonomous Student Learning||
Students must be familiar with partial differential equations.
Student must be able to use python to solve equations, calculate data and produce plots.
|Description||Timing||Component Scale||% of Final Grade|
|Class Test: Two in-class written tests||Varies over the Trimester||n/a||Standard conversion grade scale 40%||No||
|Continuous Assessment: Online quizzes||Varies over the Trimester||n/a||Standard conversion grade scale 40%||No||
|Project: Individual video project||Varies over the Trimester||n/a||Standard conversion grade scale 40%||No||
|Resit In||Terminal Exam|
|Autumn||Yes - 2 Hour|
• Group/class feedback, post-assessment
• Online automated feedback
Not yet recorded.
|Dr Dan Lucas||Lecturer / Co-Lecturer|