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Mathematical Sciences Institute · ANU College of Systems and Society
The use of mathematical models has grown rapidly in recent years, owing to the advent of cheap and powerful computers, expanding from applications in the physical and earth sciences to the biological and environmental sciences, and now into industry and commerce. In this course we study: - The process of starting with an initial succinct non-mathematical description of a problem Formulate associated mathematical models. - Introduce new mathematical techniques and then determine and interpret solutions that are useful in a real life context. - General computational and mathematical techniques and strategies will be introduced by examining specific scientific and industrial problems. The topics to be covered in this course include: - Model type selection and formulation - Data analysis techniques (time/space and frequency domain) - State Space and Transfer Function Models - Model Structure Identification - Testing and Sensitivity Analysis Computations will be done using modern high level scientific computing environments such as SCILAB or PYTHON. Note: Graduate students attend joint classes with undergraduates but are assessed separately.
Final exam
Learning outcomes 1, 2, 3, 5
Project designing and evaluating a model for a selected systemRegardless of performance on other assessment items, a minimum score of at least 35% on the project is required to pass the course. (This is known as a 'course hurdle'.)
Learning outcomes 1, 2, 6
Assignments (between 3 and 6 in total) demonstrating ability to apply techniques
Learning outcomes 1, 2, 3, 4, 5
Students presentations (delivering and providing peer assessment)
Learning outcomes 1, 4, 5
The expected workload will consist of approximately 130 hours throughout the semester including: - Face-to face component which may include approximately 3x1 hours (36 hours) of lectures per week and approximately 6x1 hours (6 hours) of workshops and approximately 10x2 hours (20 hours) of computer labs throughout the semester. - Approximately 68 hours of self-directed study throughout the semester, including weekly MATLAB Grader homework and other assessment tasks.
None. Preliminary Reading - Industrial Mathematics: Modeling in Industry, Science, and Government, by C. R. MacCluer - The pleasures of probability, by Richard Isaac - Mathematical modelling with case studies : a differential equation approach using Maple / Belinda Barnes and Glenn R. Fulford