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Use of the package MATLAB including matrix algebra, user-defined functions, surface plotting. Numerical methods including solutions of systems of linear equations, solution of ordinary differential equations and systems of equations, approximation techniques. Modelling projects and engineering applications.
PLEASE NOTE: The above description is incorrect as the course now uses Python, not MATLAB. For bureaucratic reasons this cannot be fixed for 2022. Here is what it should say:Use of the language Python for numerical methods, including solutions of systems of linear equations, solution of ordinary differential equations and systems of differential equations, boundary value problems, approximation techniques, area integration, statistics, random number generation, and Monte Carlo integration. Modelling projects and engineering applications.The use of mathematical modelling and computation in engineering. Numerical methods with strong emphasis on applications in engineering. The course has a strong programming component done in Python. Case studies with applications relevant to each engineering discipline will reinforce the theory seen in class.Course Information:An application-oriented course in mathematical modelling and scientific computation. Numerical methods and approximations underlie much of modern engineering and technology, such as modelling structures, aircraft, geophysical situations, design of integrated circuits, and image processing. The course will cover a range of techniques from calculus and linear algebra, together with algorithmic and programming considerations. Programming exercises will be conducted using Python. The methods covered will be applied in case studies specific to students’ chosen engineering programme.Topics covered:Mathematical modelling methods and techniques. Iterative methods for nonlinear equations; numerical solution of linear and nonlinear systems; interpolation and approximation; numerical quadrature; numerical solution of ordinary differential equations; random number generation and Monte Carlo integration. Python: matrix algebra; structured programming; writing Jupyter notebooks; user-defined functions; visualisation techniques.
Students will be able to:Develop and critically assess mathematical models of engineering problems.Implement numerical algorithms in Python in order to solve mathematical models.Use commercially available computer programs with enough theoretical knowledge to make intelligent decisions about the outputs.
(1) EMTH171, MATH170 or MATH171; (2) Subject to approval of the Dean of Engineering and Forestry.
Students must attend one activity from each section.
General information for students
Domestic fee $1,002.00
International fee $5,625.00
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see
Mathematics and Statistics