Learning Outcomes:
On successful completion of this module, students should be able to:
• Work with both symbolic and numerical computational tools for rapid modelling, analysis, simulation and visualisation.
• Work in a scientific computing environment, including the use of Linux, ssh and a command line interface.
• Integrate computational and mathematical skills for problem solving.
• Develop realistic modelling frameworks.
• Produce informative graphics and visualisation that enhance understanding of a problem.
• To identify and apply current research analysis to applied problems.
• Write, present and communicate mathematics in an applied and computational setting.
Indicative Module Content:
Topics will be drawn from a broad base. Representative topics include:
• Programming in Python and Mathematica
• Scientific computing tools including Linux, ssh and a command line interface
• Shooting methods for boundary value problems
• Finite difference methods
• Symbolic computer algebra
• Matched asymptotic expansions