  Topology design optimization offers tremendous opportunity in design and
manufacturing freedoms by designing and producing a part from the ground-up
without a meaningful initial design as required by conventional shape design
optimization approaches. Ideally, with adequate problem statements, to
formulate and solve the topology design problem using a standard topology
optimization process, such as SIMP (Simplified Isotropic Material with
Penalization) is possible. In reality, an estimated over thousands of design
iterations is often required for just a few design variables, the conventional
optimization approach is in general impractical or computationally unachievable
for real world applications significantly diluting the development of the
topology optimization technology. There is, therefore, a need for a different
approach that will be able to optimize the initial design topology effectively
and rapidly. Therefore, this work presents a new topology design procedure to
generate optimal structures using an integrated Generative Adversarial Networks
(GANs) and convolutional neural network architecture.
