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Curricular information is subject to change
Students will learn:
• To work with both symbolic and numerical computational tools for rapid modelling, analysis, simulation and visualisation.
• To integrate computational and mathematical skills for problem solving.
• To develop realistic modelling frameworks.
• To produce informative graphics and visualisation that enhance understanding of a problem.
• To manipulate and perform an analysis of data.
• To identify and apply current research analysis to applied problems.
• To write, present and communicate mathematics in an applied setting.
Topics will be drawn from a broad base. Representative topics include: bifurcations and chaos, stochastic differential equations, applied queuing theory, agent-based models, nonlinear waves, patterns and solitons, asymptotic methods, optimization, social networks.
Student Effort Type | Hours |
---|---|
Lectures | 12 |
Computer Aided Lab | 24 |
Specified Learning Activities | 36 |
Autonomous Student Learning | 36 |
Total | 108 |
Students must have taken ACM20030 Computational Science or an equivalent.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Class Test: Two computer-based coding exams | Throughout the Trimester | n/a | Standard conversion grade scale 40% | No | 40 |
Project: Three mini-projects drawn from the material covered in the lectures. | Throughout the Trimester | n/a | Standard conversion grade scale 40% | No | 60 |
Resit In | Terminal Exam |
---|---|
Spring | No |
• Group/class feedback, post-assessment
• Online automated feedback
Not yet recorded.