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The published literature on topology optimization has exploded over the lasttwo decades to include methods that use shape and topological derivatives orevolutionary algorithms formulated on various geometric representations andparametrizations. One of the key challenges of all these methods is the massivecomputational cost associated with 3D topology optimization problems. Weintroduce a transfer learning method based on a convolutional neural networkthat (1) can handle high-resolution 3D design domains of various shapes andtopologies; (2) supports real-time design space explorations as the domain andboundary conditions change; (3) requires a much smaller set of high-resolutionexamples for the improvement of learning in a new task compared to traditionaldeep learning networks; (4) is multiple orders of magnitude more efficient thanthe established gradient-based methods, such as SIMP. We provide numerous 2Dand 3D examples to showcase the effectiveness and accuracy of our proposedapproach, including for design domains that are unseen to our source network,as well as the generalization capabilities of the transfer learning-basedapproach. Our experiments achieved an average binary accuracy of around 95% atreal-time prediction rates. These properties, in turn, suggest that theproposed transfer-learning method may serve as the first practical underlyingframework for real-time 3D design exploration based on topology optimization

Real-Time Topology Optimization in 3D via Deep Transfer Learning