NUMERICAL SOLUTION OF PDES - 2020/1
Module code: MAT3015
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Partial differential equations (PDEs) may be used to model many physical and biological processes. Although there are some analytical solutions available for PDEs, many PDEs cannot be easily solved by hand and computational techniques play an important role in understanding and interpreting the behaviour of a given PDE.
In this module some numerical methods for solving PDEs are examined, including underlying theory and their application.
DOUGLAS Natalie (MathsMaths)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 6
JACs code: G100
Module cap (Maximum number of students): N/A
Prerequisites / Co-requisites
MAT2001 Numerical and Computational Methods MAT2011 Linear PDEs
The module will include the following:
- Finite difference methods – derivation, notions of accuracy, consistency and stability.
- Euler's method; the theta method; and the Crank-Nicolson method.
- The leapfrog method; the Lax Wendroff method; and the Lax Equivalence Theorem.
- Finite element methods, spectral methods.
- Gaining experience of writing and running code to solve partial differential equations using MatLab.
|Assessment type||Unit of assessment||Weighting|
|Examination||EXAMINATION - 2 HOURS||80|
The assessment strategy is designed to provide students with the opportunity to demonstrate
· Subject knowledge through the recall of key definitions, theorems and methods.
· Analytical ability through the solution of unseen problems in the test and exam.
· Practical programming through the solution of guided problems in coursework.
Thus, the summative assessment for this module consists of:
· One two hour examination (two of three best answers contribute to exam mark) at the end of Semester 1; worth 80% module mark.
· One coursework; worth 20% module mark.
Formative assessment and feedback
Students receive written feedback via two marked unassessed coursework assignments over an 11 week period. In addition, verbal feedback is provided by lecturer/class tutor.
- The aim of this module is to introduce the basic principles behind applying finite difference methods and finite element methods to solve partial differential equations.
|1||Know how to apply numerical methods to solve partial differential equations||KC|
|2||Be able to write MatLab code to solve simple cases.||KCPT|
|3||Understand the notions of convergence, accuracy and stability.||KC|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Overall student workload
Independent Study Hours: 117
Lecture Hours: 25
Laboratory Hours: 8
Methods of Teaching / Learning
The learning and teaching strategy is designed to provide:
- A detailed introduction to numerical methods and their analysis.
- Experience (through demonstration) of the methods used to analyse, understand and solve problems involving numerical methods.
The learning and teaching methods include:
- 25 lectures/tutorials, 8 labs.
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Reading list for NUMERICAL SOLUTION OF PDES : http://aspire.surrey.ac.uk/modules/mat3015
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2020/1 academic year.