Overview
An introduction to mathematical and computational modelling with applications in science, engineering, biomedicine and industry. Topics include the translation of observations into mathematical models, and the use of simulation and numerical methods to evaluate and apply the models.
How do you measure the thickness of gladwrap with a ruler? And what does that have to do with lethal epidemics, tidal turbines, stock options, genetic selection, machine learning and the flooding of the Leith?
These are all examples used in COMO 101 to teach techniques of mathematical and computational modelling. This paper provides essential skills for science majors and for mathematicians wanting to make use of what they’re studying.
In COMO 101, Modelling and Computation, students learn how to use mathematical and computational ideas to tackle practical problems. The paper introduces core modelling skills: how to construct models, how to simulate and compute with them, how to fit them to data, and how to assess their reliability. It also explores the use and abuse of models: How good are they? What assumptions are built in? What can they tell us — and what can they hide?
Designed especially for science majors, COMO 101 gives students tools that are often assumed but not always explicitly taught in science papers. It will change the way students think about mathematics in science.
The innovative approach developed for COMO 101 has introduced thousands of students to the practice of mathematical modelling, and has been recognised internationally as an example of best practice in modelling education.
About this paper
| Paper title | Modelling and Computation |
|---|---|
| Subject | Computational Modelling |
| EFTS | 0.15 |
| Points | 18 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees ( NZD ) | $1,318.20 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Restriction
- COMO 103
- Schedule C
- Science
- Contact
For more information, contact Professor David Bryant, david.bryant@otago.ac.nz
- Teaching staff
- Paper Structure
- Main topics:
- Introduction to estimation and mathematical modelling
- Difference equations and dynamical models
- Data fitting and numerical methods
- Randomness and stochastic models
- Uncertainty quantification
- Teaching Arrangements
Three lectures per week and a one-hour computer lab per week.
- Textbooks
Textbooks are not required for this paper.
- Graduate Attributes Emphasised
- Interdisciplinary perspective, Lifelong learning, Scholarship, Communication, Critical thinking, Environmental literacy.
View more information about Otago's graduate attributes. - Learning Outcomes
Students who successfully complete this paper will develop skills in:
- Mathematical and computational modelling
- Interdisciplinary thinking
- Communication and writing