Introduction
Cyborg designers
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What you should know
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Versions and credits
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1. What Is Generative Design?
Defining generative design
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Measurable design goals
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Design parameters
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Solution space
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Limitations of generative design
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2. Genetic/Evolutional Solver Example
Brute force: How evolution works
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Common evolutionary solvers
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Setting up Galapagos
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Running Galapagos
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Strengths and limitations of genetic/evolutional solvers
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3. Physics Solver Example
Springs: How physics solvers work
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Installing Kangaroo, Weaverbird, and Meshedit
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Kangaroo goals
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Testing and adjusting goals
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Strengths and limitations of physics solvers
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4. Machine Learning Solver Example
Introduction to machine learning
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Machine learning tools
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Regression and predictive statistics
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Clustering
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Classification
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Strengths and limitations of machine learning solvers
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5. Applying Generative Design
Design requirements and diagramming
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Sine surface points
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Roof surface
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Sides views and fitness value
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Optimizing with Galapagos
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ML structural regions
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Roof panel clusters
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Roof panel physics and classification
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Structure for optimization
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Goals and Kangaroo solver
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Visualization
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Adjustment and refinement
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Ex_Files_Grasshopper_Generative_Design_Architecture.zip
(5.9 MB)