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Generative Modelling

National University of Singapore via 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.

Taught by

Patrick Janssen, Derek Pung and Pradeep Alva


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