TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain

• URL: http://arxiv.org/abs/2003.04685v2
• Date: Wed, 11 Mar 2020 05:59:28 GMT
• Title: TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain
• Authors: Zhenguo Nie, Tong Lin, Haoliang Jiang, Levent Burak Kara
• Abstract summary: We propose a new data-driven topology optimization model called TopologyGAN. TopologyGAN 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 $3times$ reduction in the mean squared error and a $2.5times$ reduction in the mean absolute error on test problems involving previously unseen boundary conditions.
• Score: 2.0263791972068628
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In topology optimization using deep learning, 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 $3\times$ reduction in the mean squared error and a $2.5\times$ 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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