Computational Design
Using algorithms and computing power to explore, analyze, and optimize architectural designs.
Definition
Computational Design applies algorithmic thinking and computing power to architectural and engineering problems. Enhanced by AI, computational design goes beyond executing predefined rules to learn patterns, predict outcomes, and generate novel solutions. This approach enables performance-driven design where buildings are shaped by environmental, structural, and functional criteria analyzed computationally, rather than purely aesthetic or conventional considerations.
In Depth
Computational design uses algorithms and scripting to explore design options, optimize performance, and generate complex geometries that would be impractical to develop manually. AI extends computational design from expert-driven scripting (Grasshopper, Dynamo) to accessible tools that broader design teams can use.
The parametric design workflow creates a system of relationships — the floor plate responds to the structural grid, which responds to the span requirements, which respond to the program. Changing one parameter cascades through the system, generating an updated design that maintains all of the defined relationships. AI adds performance evaluation to this cascade, so each generated option is immediately evaluated for energy performance, structural efficiency, code compliance, and cost.
For complex building envelopes — parametric facades, free-form roofs, and irregular geometries — computational design is essential because these forms cannot be rationalized into buildable panels and members manually. AI assists with panelization (dividing a freeform surface into flat or single-curved panels that can be fabricated), structural analysis (verifying that the form can be supported efficiently), and constructability assessment (evaluating whether the proposed geometry can be built with available fabrication and installation methods).
Examples
Using simulations to optimize building form for wind and daylight
Algorithmic space planning based on program relationships
Computational analysis of structural load paths to inform design
Nomic Use Cases
See how Nomic applies this in production AEC workflows:
Frequently Asked Questions
Computational Design applies algorithmic thinking and computing power to architectural and engineering problems. Enhanced by AI, computational design goes beyond executing predefined rules to learn patterns, predict outcomes, and generate novel solutions. This approach enables performance-driven design where buildings are shaped by environmental, structural, and functional criteria analyzed computationally, rather than purely aesthetic or conventional considerations.
Using simulations to optimize building form for wind and daylight. Algorithmic space planning based on program relationships. Computational analysis of structural load paths to inform design.
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