Abstract/Summary

In architectural design, parametric models often include numeric parameters that can be adjusted to explore different design options. The resulting design space can be easily displayed to the user if the number of parameters is low, for example using a simple two or three-dimensional plot. However, visualising the design space of models defined by multiple parameters is not straightforward. In this paper it is shown how dimensionality reduction can assist in this task whilst retaining associativity between input designs in a high-dimensional parameter space. A form of dimensionality reduction based on neural networks, the Self-Organising Map (SOM) is used in combination with Rhino Grasshopper to demonstrate the approach and its potential benefits for human/machine design exploration.