Title: Progressive-Growing of Generative Adversarial Networks for Metasurface Optimization Show Chinese title

Title: 生成对抗网络在超表面优化中的渐进增长

Abstract: Generative adversarial networks, which can generate metasurfaces based on a training set of high performance device layouts, have the potential to significantly reduce the computational cost of the metasurface design process. However, basic GAN architectures are unable to fully capture the detailed features of topologically complex metasurfaces, and generated devices therefore require additional computationally-expensive design refinement. In this Letter, we show that GANs can better learn spatially fine features from high-resolution training data by progressively growing its network architecture and training set. Our results indicate that with this training methodology, the best generated devices have performances that compare well with the best devices produced by gradient-based topology optimization, thereby eliminating the need for additional design refinement. We envision that this network training method can generalize to other physical systems where device performance is strongly correlated with fine geometric structuring. Show Chinese abstract

Abstract: 生成对抗网络可以根据高性能器件布局的训练集生成超表面，有潜力显著降低超表面设计过程的计算成本。 然而，基本的GAN架构无法完全捕捉拓扑复杂超表面的详细特征，因此生成的器件需要额外的计算成本高昂的设计优化。 在本文中，我们表明通过逐步扩展其网络架构和训练集，GAN可以从高分辨率训练数据中更好地学习空间精细特征。 我们的结果表明，通过这种训练方法，生成的最佳器件性能可以与基于梯度的拓扑优化生成的最佳器件相媲美，从而消除了额外设计优化的需要。 我们设想这种网络训练方法可以推广到其他物理系统，在这些系统中，器件性能与精细几何结构密切相关。