  The published literature on topology optimization has exploded over the last
two decades to include methods that use shape and topological derivatives or
evolutionary algorithms formulated on various geometric representations and
parametrizations. One of the key challenges of all these methods is the massive
computational cost associated with 3D topology optimization problems. We
introduce a transfer learning method based on a convolutional neural network
that (1) can handle high-resolution 3D design domains of various shapes and
topologies; (2) supports real-time design space explorations as the domain and
boundary conditions change; (3) requires a much smaller set of high-resolution
examples for the improvement of learning in a new task compared to traditional
deep learning networks; (4) is multiple orders of magnitude more efficient than
the established gradient-based methods, such as SIMP. We provide numerous 2D
and 3D examples to showcase the effectiveness and accuracy of our proposed
approach, including for design domains that are unseen to our source network,
as well as the generalization capabilities of the transfer learning-based
approach. Our experiments achieved an average binary accuracy of around 95% at
real-time prediction rates. These properties, in turn, suggest that the
proposed transfer-learning method may serve as the first practical underlying
framework for real-time 3D design exploration based on topology optimization
