FIT3139 Computational modelling and simulation
Faculty of Information Technology
FIT3139 Computational modelling and simulation is a level 3, 6-credit-point, undergraduate unit from the Faculty of Information Technology, offered in 2020 in Semester 1 at Clayton. It has no prerequisites.
- Credit points
- 6
- Offered in 2020
- Semester 1
- Clayton
- Workload
- 12 hours
- per semester
This is the 2020 handbook entry. See the 2027 entry.
Reviews
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Requisites
Before FIT3139
No prerequisites or corequisites besides the enrolment rules below.
After FIT3139
No unit lists FIT3139 as a prerequisite in the 2020 handbook.
Enrolment rules
Prerequisites: 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)
Overview
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.
Offerings in 2020
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | On campus |
| First semester (Fully flex) | Clayton | Flexible |
Learning outcomes
When you finish this unit, you 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.
Workload and teaching
Minimum total expected workload equals 12 hours per week comprising:
(a.) Contact hours for on-campus students:
- One 2-hour workshop
- One 2-hour laboratory
- One 2-hour tutorial
(b.) Additional requirements (all students):
- A minimum of 6 hours independent study per week for completing lab, tutorial and assignment work, private study and revision.
Where it fits
FIT3139 is part of 6 areas of study in the 2020 handbook.
- COMPSCI03Additional computer science unitsAdvanced computer scienceNo reviews yet
- COMPUSC05Level 2 and 3 computational scienceComputational scienceNo reviews yet
- COMPUSC06Additional computational science unitsComputational scienceNo reviews yet
- COMPUSC07CoreComputational scienceNo reviews yet
- DATASCI01Additional data science unitsData scienceNo reviews yet
- SFTWRENG01Software engineering technical electivesSoftware engineeringNo reviews yet
Contacts
- Chief Examiners
- Dr Julian Garcia Gallego
Common questions
What are the prerequisites for FIT3139?
FIT3139 has no prerequisites, but enrolment rules apply.
When is FIT3139 offered?
In 2020, FIT3139 runs in Semester 1 at Clayton.
How much work is FIT3139?
The handbook expects about 12 hours of study across the semester. No students have rated its difficulty yet.
Which majors and minors include FIT3139?
FIT3139 is part of Advanced computer science, Computational science, Data science and Software engineering.