  Current performance-driven building design methods are not widely adopted
outside the research field for several reasons that make them difficult to
integrate into a typical design process. In the early design phase, in
particular, the time-intensity and the cognitive load associated with
optimization and form parametrization are incompatible with design exploration,
which requires quick iteration. This research introduces a novel method for
performance-driven geometry generation that can afford interaction directly in
the 3d modeling environment, eliminating the need for explicit parametrization,
and is multiple orders faster than the equivalent form optimization. The method
uses Machine Learning techniques to train a generative model offline. The
generative model learns a distribution of optimal performing geometries and
their simulation contexts based on a dataset that addresses the performance(s)
of interest. By navigating the generative model's latent space, geometries with
the desired characteristics can be quickly generated. A case study is
presented, demonstrating the generation of a synthetic dataset and the use of a
Variational Autoencoder (VAE) as a generative model for geometries with optimal
solar gain. The results show that the VAE-generated geometries perform on
average at least as well as the optimized ones, suggesting that the introduced
method shows a feasible path towards more intuitive and interactive early-phase
performance-driven design assistance.
