Journal
JOURNAL OF MECHANICAL DESIGN
Volume 143, Issue 3, Pages -Publisher
ASME
DOI: 10.1115/1.4049533
Keywords
artificial intelligence; design optimization; generative design; machine learning
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TopologyGAN, a new data-driven topology optimization model, utilizes various physical fields as inputs to achieve better accuracy on test problems compared to the baseline cGAN.
In topology optimization using deep learning, the 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 x reduction in the mean squared error and a 2.5 x 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.
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