Computational Design

Computational design is a methodology that uses algorithms, data, and computational processes to generate, analyze, and optimize design solutions across various fields such as architecture, engineering, product design, and digital art. It involves writing scripts or using visual programming tools to automate design tasks, explore complex geometries, and simulate performance criteria. This approach enables designers to handle large-scale parametric variations and data-driven decision-making efficiently.

Also known as: Parametric Design, Algorithmic Design, Generative Design, Digital Design, Comp Design

Why learn Computational Design?

Developers should learn computational design when working on projects that require generative design, parametric modeling, or optimization in fields like architecture, manufacturing, or game development. It is particularly useful for automating repetitive design tasks, exploring design spaces through algorithmic exploration, and integrating performance analysis (e.g., structural, environmental) directly into the design process. This skill enhances creativity and efficiency in industries where customization and data-driven insights are critical.

Learning Resources

• docs: Computational Design: An Introduction
• tutorial: Grasshopper Primer
• course: Parametric Design with Python
• video: Generative Design: When to Use It