论文标题
连续培训生成的对抗网络，以设计光学斗篷
Successive Training of a Generative Adversarial Network for the Design of an Optical Cloak
论文作者
论文摘要
我们提出了一种基于深卷积生成对抗网络（DCGAN）来设计二维光学斗篷的优化算法。光学斗篷由均匀和各向同性介电材料的外壳组成，披肩是通过壳的几何形状实现的。我们使用来自DCGAN解决方案的反馈循环依次重新训练，并提高其预测和找到最佳几何形状的能力。
We present an optimization algorithm based on a deep convolution generative adversarial network (DCGAN) to design a 2-Dimensional optical cloak. The optical cloak consists in a shell of uniform and isotropical dielectric material, and the cloaking is achieved via the geometry of the shell. We use a feedback loop from the solutions of the DCGAN to successively retrain it and improve its ability to predict and find optimal geometries.
