FIT3139 - Computational modelling and simulation - 2019

6 points, SCA Band 2, 0.125 EFTSL

Undergraduate - Unit

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.


Information Technology

Chief examiner(s)

Dr Julian Garcia Gallego

Unit guides



  • Second semester 2019 (On-campus)


One of MAT1841, MAT2003, ENG1091, ENG1005, MTH1030, MTH1035 or equivalent, plus any introductory programming unit (e.g. FIT1045, FIT1048, FIT1051, FIT1053, FIT1040, FIT1002, ECE2071, TRC2400, or equivalent)


This unit provides an overview of computational science and an introduction to its central methods. It covers the role of computational tools and methods in 21st century science, emphasising modelling and simulation. It introduces a variety of models, providing contrasting studies on: continuous versus discrete models; analytical versus numerical models; deterministic versus stochastic models; and static versus dynamic models. Other topics include: Monte-Carlo methods; epistemology of simulations; visualisation; high-dimensional data analysis; optimisation; limitations of numerical methods; high-performance computing and data-intensive research.

A general overview is provided for each main topic, followed by a detailed technical exploration of one or a few methods selected from the area. These are applied in tutorials and laboratories which also acquaint students with standard scientific computing software (e.g., Mathematica, Matlab, Maple, Sage). Applications are drawn from disciplines including Physics, Biology, Bioinformatics, Chemistry, Social Science.


At the completion of this unit, students should be able to:

  1. explain and apply the process of computational scientific model building, verification and interpretation;
  2. analyse the differences between core classes of modelling approaches (Numerical versus Analytical; Linear versus Non-linear; Continuous versus Discrete; Deterministic versus Stochastic);
  3. evaluate the implications of choosing different modelling approaches;
  4. rationalise the role of simulation and data visualisation in science;
  5. apply all of the above to solving idealisations of real-world problems across various scientific disciplines.


NOTE: From 1 July 2019, the duration of all exams is changing to combine reading and writing time. The new exam duration for this unit is 2 hours and 10 minutes.

Examination (2 hours): 60%, In-semester assessment: 40%

Workload requirements

Minimum total expected workload equals 12 hours per week comprising:

  1. Contact hours for on-campus students:
    • One 2-hour workshop
    • One 2-hour laboratory
    • One 2-hour tutorial
  2. Additional requirements (all students):
    • A minimum of 6 hours independent study per week for completing lab and assignment work, private study and revision.

See also Unit timetable information

This unit applies to the following area(s) of study