  In this study, a novel topology optimization approach based on conditional
Wasserstein generative adversarial networks (CWGAN) is developed to replicate
the conventional topology optimization algorithms in an extremely
computationally inexpensive way. CWGAN consists of a generator and a
discriminator, both of which are deep convolutional neural networks (CNN). The
limited samples of data, quasi-optimal planar structures, needed for training
purposes are generated using the conventional topology optimization algorithms.
With CWGANs, the topology optimization conditions can be set to a required
value before generating samples. CWGAN truncates the global design space by
introducing an equality constraint by the designer. The results are validated
by generating an optimized planar structure using the conventional algorithms
with the same settings. A proof of concept is presented which is known to be
the first such illustration of fusion of CWGANs and topology optimization.
