Part A: Course Overview

Course Title: Advanced Mathematical Modelling

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2139

City Campus

Undergraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

 Sem 2 2006,
Sem 2 2007,
Sem 2 2008,
Sem 2 2009,
Sem 1 2010,
Sem 1 2011,
Sem 1 2012,
Sem 1 2013,
Sem 1 2014,
Sem 1 2015,
Sem 2 2016

MATH2139

City Campus

Undergraduate

171H School of Science

Face-to-Face

 Sem 2 2018

Course Coordinator: Assoc Prof John Shepherd , Dr Andrew Stacey

Course Coordinator Phone: +61 3 9925 2587 (A/Prof John Shepherd)

Course Coordinator Email: jshep@rmit.edu.au, stacey@rmit.edu.au


Pre-requisite Courses and Assumed Knowledge and Capabilities

You are expected to have capabilities consistent with the successful completion of a basic course in ordinary differential equations, as well as multi-variable calculus. Typical courses that would satisfy this are MATH1142 Calculus and Analysis 1,  MATH1144 Calculus and Analysis 2, and MATH2140 Linear Algebra and Vector Calculus.


Course Description

AdvancedMathematical Modelling presents an intermediate level introduction to the construction and analysis of non-linear mathematical models arising in real world applications. Typical are models developed to describe the flow of fluids; the evolution of single and multiple species populations; oscillatory phenomena arising in mechanical and electrical systems; and interactions in chemical and biological systems. Such models are of continuing interest in science and engineering.

The governing equations relevant to the models considered will be developed from clearly defined physical laws and then analysed using a range of techniques. Where necessary, computational tools such as Maple or Matlab will be used as an adjunct to this analysis. In all cases, the ability of the models to predict the observed physical features of the system being described will be clearly delineated.


Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes for BP083 - Bachelor of Science (Mathematics) , BP245 Bachelor of Science (Statistics) and BH119  Bachelor of Analytics (Honours):

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.


On completion of this course you should be able to:

  1. Construct the equation(s) governing the behaviour of a number of physical systems of interest;
  2. Determine exact or suitable approximate solutions by making suitable assumptions about the problem of interest;
  3. Apply appropriate computer software to this analysis,
  4. Extract the information contained in such systems by direct solution of these equations


Overview of Learning Activities

You will attend lectures, where the basic theory, together with illustrative examples will be presented by an instructor. This will be augmented with computer laboratory sessions. You will be expected to reinforce this material via provided exercise sheets and assignments, to be submitted for assessment.


Overview of Learning Resources

This course will be supported online using the Learning Hub of the Distributed Learning System (DLS). The DLS will give you access to important announcements, staff contact details, the teaching schedule, online notes, assessment timelines and past exam papers. The Learning Hub can be found at http://www.rmit.edu.au/online. We encourage you to read your student EMS e-mail and visit the Learning Hub frequently. Complementary online resources are available at http://rmit.libguides.com/mathstats


Overview of Assessment

☒This course has no hurdle requirements.

Assessment Tasks

Early Assessment Task: Weekly Hand in Class Exercises  (Weeks 2 to 12 inclusive)

Weighting 50%

This assessment task supports CLOs 1-4

Assessment 2: Final Exam

Weighting 50% 

This assessment supports CLOs 1-4