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Curricular information is subject to change
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.
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
• Applied queuing theory
Student Effort Type | Hours |
---|---|
Lectures | 12 |
Computer Aided Lab | 24 |
Specified Learning Activities | 36 |
Autonomous Student Learning | 36 |
Total | 108 |
Not applicable to this module.
Resit In | Terminal Exam |
---|---|
Spring | No |
• Group/class feedback, post-assessment
• Online automated feedback
Not yet recorded.
Lecture | Offering 1 | Week(s) - Autumn: All Weeks | Mon 15:00 - 15:50 |
Lecture | Offering 1 | Week(s) - Autumn: All Weeks | Thurs 10:00 - 10:50 |
Lecture | Offering 1 | Week(s) - Autumn: All Weeks | Tues 15:00 - 15:50 |