MTH307 Mathematical Modelling (8)
AbstractThis 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.
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+ Subject Availability Modes and Location
| Session 2 | | Distance | Orange 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 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. |
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SyllabusThe 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. |
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Residential School| This subject contains a optional 2 day residential school.
Programming, data modelling
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The information contained in the 2014 CSU Handbook was accurate at the date of publication: 13 September 2013. The University reserves the right to vary the information at any time without notice.
