MTH307 Mathematical Modelling (8)
CSU Discipline Area: Mathematics and Statistics (MASTA)
Duration: One session
Abstract:
This subject is oriented towards developing the students practical skills by using computer software to solve mathematical applications in the solution of differential equations and in non-linear least-squares data modelling. Topics include: Programming; modelling with ordinary differential equations; boundary value problems; Fourier series; modelling with partial differential equations; numerical solutions of differential equations; finite differences; modelling data with non-linear least-squares regression.
+ Subject Availability Modes and Locations
| Session 2 | |
|---|---|
| Distance | Orange |
Continuing students should consult the SAL for current offering details: MTH307
Where differences exist between the Handbook and the SAL, the SAL should be taken as containing the correct subject offering details.
Prerequisite(s):
Objectives:
Upon successful completion of this subject, students should:
Be able to write computer programs to solve real-life problems;
Be able to numerically solve ordinary differential equations using various methods;
Be able to use various methods to solve partial differential equations;
Be able to use finite difference methods to numerically solve partial differential equations;
Be able to use non-linear regression for data modelling;
Be able to use suitable software for the numerical solution of differential equations.
Syllabus:
The subject will cover the following topics:
Programming with FORTRAN; Ordinary differential equations; Numerical solution of ordinary differential equations; Euler's method, Runge-Kutta method, shooting method, finite difference methods; Fourier series; Parabolic partial differential equations; separation of variables, numerical solution using finite difference methods; Hyperbolic partial differential equations; method of characteristics, separation of variables, d'Alembert's solution, numerical solution using finite difference methods; Elliptic partial differential equations; separation of variables, numerical solution using finite difference methods; Data modelling; non-linear least squares regression, numerical solution using various search methods; grid, gradient, Gauss-Newton, and mixed.
Residential School
This subject contains a optional 2 day residential school.
Programming, data modelling
The information contained in the 2013 CSU Handbook was accurate at the date of publication: 24 April 2013. The University reserves the right to vary the information at any time without notice.