Skip to main content
Unit of study_

COSC2002: Computational Modelling

2024 unit information

This unit will introduce a wide range of modelling and simulation techniques for tackling real-world problems using a computer. Data is often expensive to obtain, so by harnessing the enormous computational processing power now available to us we can answer what if questions based on data we already have. You will learn how to break a problem down into its key components, identifying necessary assumptions for the purposes of simulation. You will learn how to develop suitable metrics within computational models, to allow comparison of simulation data with real-world data. You will learn how to iteratively improve simulations as you validate them against real results, and you will gain experience in identifying the types of exploratory questions that computational modelling opens up. Programming will be in python. You will learn how to generate probabilistic data, solve systems of differential equations numerically, and tackle complex adaptive systems using agent-based models. Dynamical systems ranging from traffic flow to social segregation will be considered. By doing this unit you will develop the skills to go behind your data, understand why the data you observe might be as it is, and test scenarios which might otherwise be inaccessible.

Unit details and rules

Managing faculty or University school:

Physics Academic Operations

Code COSC2002
Academic unit Physics Academic Operations
Credit points 6
Prerequisites:
? 
None
Corequisites:
? 
None
Prohibitions:
? 
COSC1003 or COSC1903 or COSC2902
Assumed knowledge:
? 
HSC Mathematics; DATA1002, or equivalent programming experience, ideally in Python

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

  • LO1. given a real-world problem, develop a simplified model
  • LO2. identify and explain assumptions underpinning a model of a real-world problem
  • LO3. obtain a solution to a model using a computer
  • LO4. given a computational model, and simulation output, identify key characteristics of the numerical solution
  • LO5. check validity of simulation output and be able to defend conclusions
  • LO6. develop and use metrics to compare simulation and real-world data.

Unit availability

This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.

The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.

Session MoA ?  Location Outline ? 
Semester 1 2024
Normal day Camperdown/Darlington, Sydney
Session MoA ?  Location Outline ? 
Semester 1 2020
Normal day Camperdown/Darlington, Sydney
Semester 1 2021
Normal day Camperdown/Darlington, Sydney
Semester 1 2021
Normal day Remote
Semester 1 2022
Normal day Camperdown/Darlington, Sydney
Semester 1 2022
Normal day Remote
Semester 1 2023
Normal day Camperdown/Darlington, Sydney

Modes of attendance (MoA)

This refers to the Mode of attendance (MoA) for the unit as it appears when you’re selecting your units in Sydney Student. Find more information about modes of attendance on our website.