MTH307 Mathematical Modelling (8)
Abstract|
This subject develops students’ practical skills by using computer software to solve mathematical applications. The two main applications considered are the numerical solutions of differential equations (ordinary and partial); and fitting data to a model using least-squares regression (linear and non-linear). |
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+ Subject Availability Modes and Location
| Session 2 | | Distance | Wagga Wagga Campus |
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.
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Subject information| Duration | Grading System | School: |
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| One session | HD/FL | School of Computing and Mathematics |
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Learning OutcomesUpon successful completion of this subject, students should:
- be able to write computer programs to solve real-life problems;
- be able to numerically calculate the solutions of ordinary differential equations using various methods;
- be able to adapt existing code to produce numerical solutions for differential equations;
- be able to generate suitable finite difference equations from differential equations;
- be able to determine the stability of a finite difference equation;
- be able to calculate the solutions of partial differential equations using various methods;
- be able to fit data to a model using linear and non-linear regression techniques;
- be able to interpret mathematical models and communicate their output to non-mathematical audiences.
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SyllabusThe subject will cover the following topics:
- Introduction to mathematical modelling.
- Programming with Maple.
- Linear regression: simple and multiple; forward selection and backwards elimination methods.
- Numerical solution of ordinary differential equations for both initial and boundary value problems; Euler's method, Runge-Kutta method, shooting method and finite difference methods.
- Fourier series.
- Partial differential equations (parabolic, hyperbolic and elliptic): separation of variables, numerical solution using finite difference methods; stability of finite difference methods and method of characteristics.
- Non-linear regression: various numerical methods: grid, gradient, Gauss-Newton and mixed.
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The information contained in the 2016 CSU Handbook was accurate at the date of publication: 06 September 2016. The University reserves the right to vary the information at any time without notice.
