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  3. Computational Design and Optimisation

Computational Design and Optimisation (ABPL90123)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

Availability
Semester 1 - On Campus
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This subject explores computational design and optimisation as methods for generating, evaluating, and refining architectural design proposals. Students engage with parametric modelling, rule-based systems, and algorithmic thinking to develop computational workflows that are both iterative and performance-oriented.

Topics include pseudo-randomness and parametric logic, structural and environmental optimisation, and the use of optimisation algorithms to explore design variations and support informed decision-making. Emphasis is placed on integrating computational techniques into architectural design processes, bridging conceptual thinking with technical execution.

While little to no prior experience with computational design is assumed, students are expected to independently learn the required tools and platforms (e.g., Rhinoceros 3D and Grasshopper). The subject moves at a fast pace, and active engagement with the technical content is essential for success.

Students will learn core computational design techniques, frame design problems computationally, apply optimisation and machine learning methods, and critically examine the interplay between design thinking and design computing.

Prescribed software programs with a cost
McNeel Rhino

Prescribed software tools 
Image editing software 
Vector editing software 
Layout software

Details of software availability and pricing are captured at: https://msd.unimelb.edu.au/current-students/student-experience/it-support

 

 

Intended learning outcomes

On completion of the subject, students should be able to:

  • Describe a variety of computational design techniques, and identify both challenges and limits of using such techniques in real-world scenarios;
  • Formulate a design problem in computational terms;
  • Apply a variety of optimisation and machine learning techniques to solve design problems
  • Critically reflect on and articulate the relationship between design thinking and design computing.

Generic skills

  • Proficiency in the use of computational design tools for design automation (scripting), optimisation, and machine learning.
  • Critical thinking, reflection, and analysis related to computational design tools, processes, and theories.
  • Problem-solving skills applied to computational design contexts.
  • Ability to communicate a computational design process through diagrams, code, and written explanation.

Last updated: 19 November 2025