Coding Growth: Scripting & Computational Methods for Design

3 units

The course will cover an array of advanced design techniques using scripting within the rhino/grasshopper environment. Although, thorough knowledge of grasshopper is not required, it will help to ease the transition away from graphic algorithm creation, and into the C# scripting language. C#, while slightly more difficult than its technical equal, VB, the language can be used in a wide variety of other programs and applications, such as 'Processing and the Arduino micro-controller, which students will have the opportunity to explore should they feel comfortable in those environments. The foundation of the class will be based around three major categories: Mathematical Systems, Agent-Based systems, and simulated Growth. Before covering the base material, a three-week intensive overview of grasshopper will cover nearly everything students will need to know in order to proceed successfully. We will begin basic scripting exercises by duplicating existing grasshopper components and proceed to the advanced categories from there. In the first category, mathematical systems, students will learn how to create and control useful L-Systems like branching, fractals, and generational scaling. We will then move into topological algorithms, in order to learn how to sort and search through the scripted results to quantify their success or failure. Once single codes can be executed relatively successfully, the course will progress into agent-based systems. The topic will be introduced through simple investigations in basic geometrical relationships like circle packing and mesh relaxation. We will then study the behaviors of birds, ants, fish, and termites in order to extract the necessary parameters to mimic their behavior. Building complexity yet again, students will investigate the ways in which one can code growth. This will ultimately lead to an architectural project at the pavilion scale where students will be asked to design a single unit that will mutate and deform itself iteratively in order to achieve explicit performance criteria that have been laid out for them. Students will learn how to use genetic and generative algorithms, (a combination of the previous lessons) to design the building unit and appropriate transformation criteria, and, hopefully, by the end of all of this, each student will have designed a site-adaptable, feasible pavilion without knowing what that pavilion will look like in any given application.