# Course Title: Mathematical Modelling

## Part A: Course Overview

Course Title: Mathematical Modelling

Credit Points: 12.00

## Terms

### Teaching Period(s)

MATH2194

City Campus

145H Mathematical & Geospatial Sciences

Face-to-Face

Sem 1 2011,
Sem 1 2012,
Sem 1 2013,
Sem 1 2014,
Sem 1 2015,
Sem 1 2016

MATH2194

City Campus

171H School of Science

Face-to-Face

Sem 1 2018,
Sem 1 2019,
Sem 1 2020

Course Coordinator: Hayden McQueenie

Course Coordinator Phone: -

Course Coordinator Email: hayden.mcqueenie@rmit.edu.au

Course Coordinator Location: -

Course Coordinator Availability: Email for appointment

Pre-requisite Courses and Assumed Knowledge and Capabilities

This course assumes that you have achieved a passing grade in the RMIT courses MATH1142 and MATH1144, or equivalent.

Course Description

Mathematical Modelling is a single semester course introducing you to powerful techniques used to assist in determining a mathematical representation of an engineering problem. The specified mathematical model endeavours to reflect the known features of the application being modelled, as well as predicting the system’s behaviour in other circumstances. This course will integrate theory and application using a problem-based approach. This course also prepares you for future learning in relation to problem solving and decision-making; technical competence; teamwork and leadership; and reflection.

Objectives/Learning Outcomes/Capability Development

On successful completion of this course, you will be able to:

• Assemble a mathematical model for a range of physical situations.
• Non-dimensionalise and scale the (differential) equations which represent a mathematical model.
• Apply analytical techniques to solve a mathematical model, i.e. perform the calculations needed to obtain a solution or a suitable approximation.
• Critically analyse the effectiveness of any differential equation in modelling specified situations recognising that useful qualitative information in the behaviour of a system can often be gleaned from suitably "crude" models.
• Suggest modifications that may improve the theoretical "fit" to the actual application being modelled being mindful of any added complexity to the efficacy of then being able to generate a feasible solution.
• Reflect upon your own goals, learning and time management skills.

This course contributes to the development of the following Program Learning Outcomes:

Knowledge and technical competence

• The ability to use the appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.

Problem-solving

• The ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
• An understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.

Overview of Learning Activities

This course requires you to learn and develop diverse abilities including abstract modelling of physical problems and translating simple word problems into mathematical equations. Learning activities will involve attending and participating in lectures, practice classes and tests .

You will be provided with the opportunity to clarify main concepts through questions that are designed to promote teamwork and critical thinking. You will be encouraged to work in small groups, but to present your own solution to each set task. This will be achieved through practice class sessions where you will receive feedback whilst attempting to formulate mathematical models and to determine their solutions. It will provide a forum for you to discuss your solution strategies with colleagues and in these discussions to develop your analytical ability and communication skills. Staff members will oversee these activities responding when necessary.

Overview of Learning Resources

You will have access to learning resources comprising the recommended references, a set of detailed course notes and other relevant materials such as extra notes, assignments, past exam papers and their solutions. These are available online via the RMIT Learning Hub (my RMIT). You will also have access to RMIT Library online and other hardcopy resources.  A series of tutorial sheets with detailed solutions on relevant text problems will also be provided on a weekly basis.

Library Subject Guide for Mathematics & Statistics http://rmit.libguides.com/mathstats

Overview of Assessment

This course has no hurdle requirements.   Assessment Tasks Early Assessment Task: Tutorials 1 & 2 Weighting 6% This assessment task supports CLOs 1 & 2   Assessment Task 2: Tutorials 3 7  Weighting 15 % This assessment task supports CLOs 1 - 5   Assessment Task 3: Tests 1 & 2 Weighting 29% This assessment task supports CLOs 1 - 5   Assessment Task 4: Exam  Weighting 50%  This assessment supports CLOs 1 - 6