## COMO101 Modelling and Computation

Second Semester |

COMO 101 is on blackboard: go to https://blackboard.otago.ac.nz/ for up-to-date course information, lectures, tutorial sheets, etc.

This paper introduces mathematical modelling and associated scientific computation, with applications across a wide range of topics in every-day life, science, engineering, biomedicine, and business. Mathematical modelling requires translating ambiguous problems into precise mathematical models, and the use of simulation and numerical methods to evaluate solutions and make predictions from the models.

If you want to check out the paper before enrolling, you're welcome to attend the first few lectures. They are in

- BURNS 2, Monday at 2pm
- ARCHWAY 1, Tuesday at 2pm
- BURNS 1, Wednesday at 2pm.

Note: there are no tutorials in the first week of the semester.

### Timetable

(1) Mathematical modelling basics (2 weeks).

Constructing a mathematical model requires making assumptions. How do we make sure those assumptions are sensible and the model is correct? Scientists often carry out “back of the envelope” calculations to get a rough answer to a problem before proceeding to more exact and involved approaches. Working with a few assumptions and a number of rough measurements, we can often make some startlingly accurate predictions and estimates. We use examples to introduce the main notions of mathematical modelling, its weaknesses and strengths, and identify common steps. The examples we use range from an analysis of a potential hydro scheme in Otago harbour to how we can measure the thickness of gladwrap with a school ruler.

(2) Difference equations and dynamical models (4 weeks)

Difference equations describe, mathematically, how quantities change over time. We introduce the basic ideas required to set up difference equations and systems of difference equations, drawing on examples from population growth, epidemiology, ecology, and genetics. The emphasis is on simulation of the systems and qualitative analysis, rather than analytical solutions.

(3) Randomness and stochastic models (3 weeks)

Stochastic models – that include randomness as part of the mathematical model – are incredibly useful for modelling systems with uncertain inputs or parameters. Average or long-term behaviour can be determined by simulating the stochastic model. The growing importance of simulation is one of the most important developments in model-based inference and indeed in statistical computing in general. We review relevant basics from probability theory, with an emphasis on how to simulate random variables and processes. We demonstrate how simulation can be used to estimate quantities and integrals.

(4) Data fitting and numerical methods (3 weeks)

In this section we will see how numerical methods can be used to determine model parameters from data, and explore these ideas through a diverse range of applications and case studies. We look at how to determine if our inferences are sensitive to small changes in parameters, and introduce the thorny area of model comparison.

Note: this timetable is provisional, and is subject to change and improvement.

### Prerequisites

None, though students will be expected to do some algebraic manipulation. Students who have taken MATH 160 or similar tend to find many of the problems easier.

### Lecturers

A/Prof Colin Fox, Department of Physics, Room 523, Science III. Colin's main areas of research expertise are mathematical physics, computational Bayesian statistics, inverse problems, and acoustics.

Prof David Bryant, Department of Mathematics and Statistics, Room 232a, Science III. David's main area of research expertise is the application and development of mathematical, computational and statistical techniques in evolutionary biology and genetics.

### Labs/tutorials

Labs/tutorials are at 4pm on Wednesdays, and 9am, 10am, 1pm, 2pm on Thursdays. All labs run for one hour. Some labs will require group work. There will be no tutorials in the first week of lectures.

### Assessment

Your final grade will combine internal assessment and the final exam. The breakdown of assessment is:

Assignments (four) 5% each

Midterm test 10%

Tutorials 10%

Final Exam 60%

The Midterm (theory) test will be held Wednesday 24th August, 2pm. The room will be announced shortly.

Assignments must be submitted electronically through Blackboard. Late assignments will not be accepted.

### Missed assessments

#### Tutorials.

If you can't come to your allocated tutorial in a week, come to one of the other tutorials. They are Wed 4pm, Thu 9,10,1,2, and all in MATH 242. If you're sick and can't come to a tutorial, complete the tutorial in your own time and get it checked off in the following week.

#### Assignment.

If you miss an assignment for ill health, you'll need a medical certificate. We will take the grades for your other assignments and average them to give the grade for this assignment.

#### Midterm test.

There will be no make-up or test or alternative date. If you miss the midterm because of ill health or because you're away (check this with me first) we will rescale the final exam to cover your midterm.

If you have long-term illness or some other significant issues which are getting in the way of your COMO 101 work, please let use know, or go direct to student disabilities.

### Terms Requirement

No terms requirement.

#### Exam format

The COMO 101 final examination will be three hours long. You will be permitted to take approved calculators into the exam, but no notes or communicating devices.

Required text

There will be no required textbook.

### Final mark

Your final mark F in the paper will be calculated according to this formula:

**F = 0.2A + 0.1T + 0.1M + 0.6E**

where:

- E is the Exam mark
- A is the Assignments mark
- T is the Tutorials mark
- M is the Midterm mark

and all quantities are expressed as percentages.

### Students must abide by the University’s Academic Integrity Policy

**Academic integrity** means being honest in your studying and assessments. It is the basis for ethical decision-making and behaviour in an academic context. Academic integrity is informed by the values of honesty, trust, responsibility, fairness, respect and courage.

**Academic misconduct** is seeking to gain for yourself, or assisting another person to gain, an academic advantage by deception or other unfair means. The most common form of academic misconduct is plagiarism.

Academic misconduct in relation to work submitted for assessment (including all course work, tests and examinations) is taken very seriously at the University of Otago.

All students have a responsibility to understand the requirements that apply to particular assessments and also to be aware of acceptable academic practice regarding the use of material prepared by others. Therefore it is important to be familiar with the rules surrounding academic misconduct at the University of Otago; they may be different from the rules in your previous place of study.

Any student involved in academic misconduct, whether intentional or arising through failure to take reasonable care, will be subject to the University’s Student Academic Misconduct Procedures which contain a range of penalties.

If you are ever in doubt concerning what may be acceptable academic practice in relation to assessment, you should clarify the situation with your lecturer before submitting the work or taking the test or examination involved.

Types of academic misconduct are as follows:

**Plagiarism**

The University makes a distinction between unintentional plagiarism (Level One) and intentional plagiarism (Level Two).

- Although not intended,
*unintentional plagiarism*is covered by the Student Academic Misconduct Procedures. It is usually due to lack of care, naivety, and/or to a lack to understanding of acceptable academic behaviour. This kind of plagiarism can be easily avoided. *Intentional plagiarism*is gaining academic advantage by copying or paraphrasing someone elses work and presenting it as your own, or helping someone else copy your work and present it as their own. It also includes self-plagiarism which is when you use your own work in a different paper or programme without indicating the source. Intentional plagiarism is treated very seriously by the University.

**Unauthorised Collaboration**

Unauthorised Collaboration occurs when you work with, or share work with, others on an assessment which is designed as a task for individuals and in which individual answers are required. This form does not include assessment tasks where students are required or permitted to present their results as collaborative work. Nor does it preclude collaborative effort in research or study for assignments, tests or examinations; but unless it is explicitly stated otherwise, each students answers should be in their own words. If you are not sure if collaboration is allowed, check with your lecturer..

**Impersonation**

Impersonation is getting someone else to participate in any assessment on your behalf, including having someone else sit any test or examination on your behalf.

**Falsiﬁcation**

Falsiﬁcation is to falsify the results of your research; presenting as true or accurate material that you know to be false or inaccurate.

**Use of Unauthorised Materials**

Unless expressly permitted, notes, books, calculators, computers or any other material and equipment are not permitted into a test or examination. Make sure you read the examination rules carefully. If you are still not sure what you are allowed to take in, check with your lecturer.

**Assisting Others to Commit Academic Misconduct**

This includes impersonating another student in a test or examination; writing an assignment for another student; giving answers to another student in a test or examination by any direct or indirect means; and allowing another student to copy answers in a test, examination or any other assessment.