Grasshopper: Generative Design for Architecture

Introduction

• Cyborg designers
• What you should know
• Versions and credits

1. What Is Generative Design?

• Defining generative design
• Measurable design goals
• Design parameters
• Solution space
• Limitations of generative design

2. Genetic/Evolutional Solver Example

• Brute force: How evolution works
• Common evolutionary solvers
• Setting up Galapagos
• Running Galapagos
• Strengths and limitations of genetic/evolutional solvers

3. Physics Solver Example

• Springs: How physics solvers work
• Installing Kangaroo, Weaverbird, and Meshedit
• Kangaroo goals
• Testing and adjusting goals
• Strengths and limitations of physics solvers

4. Machine Learning Solver Example

• Introduction to machine learning
• Machine learning tools
• Regression and predictive statistics
• Clustering
• Classification
• Strengths and limitations of machine learning solvers

5. Applying Generative Design

• Design requirements and diagramming
• Sine surface points
• Roof surface
• Sides views and fitness value
• Optimizing with Galapagos
• ML structural regions
• Roof panel clusters
• Roof panel physics and classification
• Structure for optimization
• Goals and Kangaroo solver
• Visualization
• Adjustment and refinement

Conclusion

• Next steps