  In topology optimization using deep learning, load and boundary conditions
represented as vectors or sparse matrices often miss the opportunity to encode
a rich view of the design problem, leading to less than ideal generalization
results. We propose a new data-driven topology optimization model called
TopologyGAN that takes advantage of various physical fields computed on the
original, unoptimized material domain, as inputs to the generator of a
conditional generative adversarial network (cGAN). Compared to a baseline cGAN,
TopologyGAN achieves a nearly $3\times$ reduction in the mean squared error and
a $2.5\times$ reduction in the mean absolute error on test problems involving
previously unseen boundary conditions. Built on several existing network
models, we also introduce a hybrid network called
U-SE(Squeeze-and-Excitation)-ResNet for the generator that further increases
the overall accuracy. We publicly share our full implementation and trained
network.
