Design Space Exploration
(by Digital Structures)
A suite of Grasshopper tools for visual, performance-based, multi-objective design space exploration
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Design Space Exploration (DSE) is a suite of open-source Grasshopper tools developed by Digital Structures at MIT.  These tools aim to support visual, performance-based design space exploration and interactive multi-objective optimization (MOO) for conceptual design.  Rather than one single component or user interface, these tools can be used flexibly with other Grasshopper components or plug-ins to take a variety of approaches to DSE and MOO, including a prioria posteriori, and interactive articulation of performance priorities.  Various DSE components allow the user to sample a parametric design space made from sliders, automatically iterate and capture images and numerical properties, reconstruct previous designs, cluster designs into families, analyze the importance of design variables, approximate computationally-intensive performance evaluations, and find Pareto fronts for multi-objective problems.   Although the components are intended to link together for simple, automated workflows for performance-based design of buildings and other structures, they can be used for any applications that require these functionalities within Grasshopper. 

Github | Digital Structures | Stormcloud | User Manual

The download for DSE comes with several related plug-ins for early design exploration.  The first is Stormcloud, which allows for parametric exploration using an interactive evolutionary framework, combining quantitative performance analysis with qualitative designer input. The most recent version of DSE also includes Radical, for constrained optimization, and Stepper, for interactive gradient-based optimization.  All of these optimization tools can work with any model and geometry type that can be represented and analyzed in Grasshopper, as long as design variables and objectives are present.

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