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M.Sc. In Mathematical Modelling

Background and Justification

Most things in the real world are really complex systems which cannot be fully understood by using linear representations. Consider, for example, the complex biological systems that make up our lives and our environment, the increasingly complex financial markets and myriad industrial processes, managing traffic flows, etc, all of which can only be understood, mimicked and predicted if we use dynamic mathematical models. Mathematical modelling, an essential part of contemporary applied mathematics, is a fundamental tool for understanding these systems and can be a powerful tool for planning purposes in modern economies. This program will both draw from existing capacity in the Mathematics Institute and add capacity to it.

Program Aims

This course has three main aims:

  • To provide an understanding of the processes involved in creating a viable mathematical model
  • To teach the fundamental analytical techniques and computational methods used in understand the behaviour of systems
  • To expose students to a range of typical applications (e.g. industrial, biological and environmental)

Program Structure

The program will consist of nine 4-credit courses to make up a total of 36 credits. All courses will be taught and assessed on the basis of any combination of continuous assessment, examinations and applied mini projects. A letter grade will be awarded for each course completed. Courses will be offered over 2 sessions, with 4 courses being offered during the first session and the second session being slightly longer than the first, in order to allow time for any project-based courses or allow sufficient time for 5 courses to be offered.

  1. Non Linear Systems
  2. Numerical Solution of Partial Differential Equations
  3. Computational and Simulation Methods
  4. Advanced Mathematical Modelling Techniques
  5. Asymptotic Models and Boundary Layer Theory
  6. Geophysical Fluid Dynamics
  7. Biomathematics
  8. Discrete Mathematics and Algorithms
  9. Stochastic dynamic modelling
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