Catalogue of Modules, University of Nottingham

G14CAM Computational Applied Mathematics
(Last Updated:03 May 2017)

Year  15/16

Total Credits: 20

Level: Level 4

Target Students:  Students taking the MSc Scientific Computing and the MSc Financial and Computational Mathematics in the School of Mathematical Sciences; Also available to Year 4 MMath students and other MSc students.

Taught Semesters:

Spring Assessed by end of Spring Semester 

Prerequisites: For MMath students, G12INM is a pre-requisite. MSc students should have a solid background in mathematics, including calculus, linear algebra, and ordinary differential equations as covered by the entry requirements. Some programming experience (Matlab, C++, Python, etc) is expected.

G12INM Introduction to Numerical Methods 

Corequisites:  None.

Summary of Content:  Four major topics for the computational solution of problems in applied mathematics are considered in this module:

The focus is on formulating and understanding computational techniques with illustrations on elementary models from a variety of scientific applications. Specific contents include

Method and Frequency of Class:

ActivityNumber Of WeeksNumber of sessionsDuration of a session
Lecture 12 weeks1 per week2 hours
Workshop 12 weeks1 per week1 hour

Activities may take place every teaching week of the Semester or only in specified weeks. It is usually specified above if an activity only takes place in some weeks of a Semester

Further Activity Details:
One 2-hour class and one 1-hour class per week timetabled centrally, which is used for lectures, example and problem classes.

Method of Assessment: 

Assessment TypeWeightRequirements
Exam 1 60 2.5 hour written examination 
Coursework 1 20 Assessed coursework including a computing component 
Coursework 2 20 Assessed coursework including a computing component 

Dr K van der Zee

Education Aims:  This module introduces computational methods for solving problems in applied mathematics. Students taking this module will develop knowledge and understanding to design, justify and implement relevant computational techniques and methodologies.

Learning Outcomes:  A student who completes this module successfully should be able to:
L1 - Formulate and analyse polynomial approximations;
L2 - Formulate and analyse computational methods for the solution of nonlinear equations;
L3 - Formulate and analyse relevant numerical methods for ODEs and PDEs;
L4 - Implement computational algorithms using a sophisticated programming language.

Offering School:  Mathematical Sciences

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