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Computational Modelling and Simulation (COMP90083)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

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Semester 2
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Computers are invaluable tools for modelling and simulating complex systems in a range of real word domains. The complex behaviours exhibited by many biological, social and technological systems - such as epidemics, urban systems and robotics - challenge our ability to predict, analyse and design such systems. Building computational models of these systems can help us better understand their structure and behaviour, and make better decisions about their design and control.

The aim of this subject is to provide students with a solid foundation in the conceptual and technical skills required to design, implement and evaluate computational models of complex systems.

INDICATIVE CONTENT

Topics covered will be selected from:

  • the use of models for science, engineering and policy
  • dynamical systems analysis
  • complexity and emergent behaviour
  • agent-based models
  • design, communication and evaluation of models
  • analysis and visualisation of model behaviour
  • case study exemplars of specific types of models, such as:
    • spatial models (eg, transportation)
    • network models (eg, epidemics)
    • adaptive models (eg, robotics)

Intended learning outcomes

On completion of this project, the student is expected to be able to:



  • Identify and abstract the key features of complex systems from a variety of domains
  • Understand the theoretical basis underpinning the analysis of complex systems
  • Evaluate and select, amongst different modelling techniques, the most appropriate for analysing specific systems
  • Create computational models to analyse the behaviour of complex systems.

Generic skills

  • Ability to undertake problem identification, formulation and solution
  • Capacity for independent critical analysis and problem-solving, including engaging with unfamiliar problems and identifying relevant strategies
  • Ability to analyse models, and self-directed research for modelling approaches
  • Intellectual curiosity and creativity, including the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency a model-based analysis.
  • Openness to new ideas and unconventional critiques of received wisdom

Last updated: 30 May 2024