Generative Modelling (edX)

As part of our “Spatial Computational Thinking” program, this “Generative Modelling” course focuses on the generation of complex spatial information models capturing various relationships and constraints. You will learn how to tackle challenging problems by integrating multiple procedures that work together to generate spatial information models.
This course will build on the previous procedural modelling course. In this course, the complexity of the spatial information modelling tasks will increase, requiring a more advanced type of generative modelling approach. You will learn advanced generative modelling techniques, such as using law curves and resolving spatial constraints by implementing your own solvers. You will learn skeletal modelling strategies that make it easier to control the complexity of the generative process.
You will also learn a range of general mathematic techniques that are critical to basic types of spatial reasoning, including working with vectors, rays, and planes, and using various mathematical functions such as periodic functions, and dot product and cross product functions. You will also revisit the debugging process, learning how flowcharts can be used to isolate errors.
In the process, you will also further develop your coding skills. You will revisit the loops and conditional and discover how these can be nested to create more complex control flows. You will also discover how list and dictionary data structures can be nested to create more complex types of data structures.
The modelling exercises and assignments during this course will also become more advanced. The spatial information models will now represent complex buildings with a range of different types of components and parts, tagged with attributes and grouped into collections.
The course prepares you for the next and final course in the "Spatial Computational Thinking” program , focusing on performative modelling.

What you'll learn

Learning algorithmic thinking:

• How parameters define a search space of possible configurations
• How to decompose a problem by breaking it down into smaller sub-problems
• Recognise underlying algorithmic principles within spatial configurations

Learning generative modelling:

• Using skeletal modelling strategies to control model complexity
• Modelling spatial relationships using law curves
• Modelling with spatial constraints, for example, Floor-Area Ratio
• Strategies for solving constraints
• Creating simple constraint solvers using ‘for loops’
• Pushing attributes through the topological hierarchy
• Visualizing models with colour and materials
• Understanding polygon normals and their impact on light
• Importing and exporting geometric and geospatial data models

Learning coding:

• Spatial reasoning with vector mathematics
• Working with infinite planes and infinite rays
• Modelling with periodic functions: sin(), cos(), tan()
• Spatial reasoning using the dot product and cross product functions
• Optimizing code to improve execution speed
• Developing complex data structures using nested lists and dictionaries
• Using nested loops and nested conditionals
• Strategies for looping: using a counter or iterating over a list?
• How to avoid deep nesting of loops using data structures

Learning Möbius Modeller:

• Strategies for creating and debugging flowcharts
• Documenting flowcharts and parameters
• Publishing flowcharts online for others to interact with and explore
• The Möbius Spatial Information data model
• Interrogating models in the 3D viewer
• Difference between local and global functions
• Creating flowcharts that can be imported as global functions

Prerequisites:
Completion of Course-1: Procedural modellingof 'Spatial Computational Thinking'Professional Certificate program.