  We propose a data-driven 3D shape design method that can learn a generative
model from a corpus of existing designs, and use this model to produce a wide
range of new designs. The approach learns an encoding of the samples in the
training corpus using an unsupervised variational autoencoder-decoder
architecture, without the need for an explicit parametric representation of the
original designs. To facilitate the generation of smooth final surfaces, we
develop a 3D shape representation based on a distance transformation of the
original 3D data, rather than using the commonly utilized binary voxel
representation. Once established, the generator maps the latent space
representations to the high-dimensional distance transformation fields, which
are then automatically surfaced to produce 3D representations amenable to
physics simulations or other objective function evaluation modules. We
demonstrate our approach for the computational design of gliders that are
optimized to attain prescribed performance scores. Our results show that when
combined with genetic optimization, the proposed approach can generate a rich
set of candidate concept designs that achieve prescribed functional goals, even
when the original dataset has only a few or no solutions that achieve these
goals.

Machine Learning;Computational Geometry;Neural and Evolutionary Computing;Machine Learning