Grasshopper: Generative Design for Architecture

3h 15mIntermediate2019-03-22

Authors

Walter Zesk

Designer, Professor, and Cofounder of Conform Lab

Course details

Generative design is a set of technologies that give you a computerized copilot for your design process, helping you engage the power of computation and algorithms to create designs. In this course, learn how to use the Grasshopper visual programming plugin with Rhino to create designs that would have been impossible in the past. Instructor Walter Zesk explains how to use physics solvers and evolutionary solvers to set goals and constraints for your designs, as well as how to use computation to meet those goals and work within your constraints. Plus, he covers how to use LunchBoxML to apply machine learning to your projects
and unleash the full power of artificial intelligence to create designs.

Learning objectives
What is generative design?
Limitations of generative design
Strengths and limitations of genetic/evolutional solvers
How physics solvers work
Testing and adjusting goals
Working with machine learning tools
Design requirements and diagramming
Optimizing with Galapagos

Skills covered

GrasshopperComputational DesignRhinoRobert McNeel & AssociatesAECProduct and ManufacturingProgramming LanguagesSoftware DevelopmentDeep Dive (X:Y)

Concepts

0. Introduction

01 - Cyborg designers
02 - What you should know
03 - Versions and credits

1. What Is Generative Design

04 - Defining generative design
05 - Measurable design goals
06 - Design parameters
07 - Solution space
08 - Limitations of generative design

2. Genetic Evolutional Solver Example

09 - Brute force - How evolution works
10 - Common evolutionary solvers
11 - Setting up Galapagos
12 - Running Galapagos
13 - Strengths and limitations of genetic evolutional solvers

3. Physics Solver Example

14 - Springs - How physics solvers work
15 - Installing Kangaroo, Weaverbird, and Meshedit
16 - Kangaroo goals
17 - Testing and adjusting goals
18 - Strengths and limitations of physics solvers

4. Machine Learning Solver Example

19 - Introduction to machine learning
20 - Machine learning tools
21 - Regression and predictive statistics
22 - Clustering
23 - Classification
24 - Strengths and limitations of machine learning solvers

5. Applying Generative Design

25 - Design requirements and diagramming
26 - Sine surface points
27 - Roof surface
28 - Sides views and fitness value
29 - Optimizing with Galapagos
30 - ML structural regions
31 - Roof panel clusters
32 - Roof panel physics and classification
33 - Structure for optimization
34 - Goals and Kangaroo solver
35 - Visualization
36 - Adjustment and refinement

Conclusion

37 - Next steps