Release date:2019, March 22

Duration:03 h 15 m

Author:Walter Zesk

Skill level:Beginner

Language:English

Exercise files:Yes

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.

Topics include:

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

01 Introduction 001 Cyborg designers 002 What you should know 003 Versions and credits

02 What Is Generative Design? 001 Defining generative design 002 Measurable design goals 003 Design parameters 004 Solution space 005 Limitations of generative design

03 Genetic/Evolutional Solver Example 001 Brute force_ How evolution works 002 Common evolutionary solvers 003 Setting up Galapagos 004 Running Galapagos 005 Strengths and limitations of genetic_evolutional solvers

04 Physics Solver Example 001 Springs_ How physics solvers work 002 Installing Kangaroo Weaverbird and Meshedit 003 Kangaroo goals 004 Testing and adjusting goals 005 Strengths and limitations of physics solvers

05 Machine Learning Solver Example 001 Introduction to machine learning 002 Machine learning tools 003 Regression and predictive statistics 004 Clustering 005 Classification 006 Strengths and limitations of machine learning solvers

06 Applying Generative Design 001 Design requirements and diagramming 002 Sine surface points 003 Roof surface 004 Sides views and fitness value 005 Optimizing with Galapagos 006 ML structural regions 007 Roof panel clusters 008 Roof panel physics and classification 009 Structure for optimization 010 Goals and Kangaroo solver 011 Visualization 012 Adjustment and refinement

07 Conclusion 001 Next steps

Exercise Files.zip

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