用于光学隐身衣设计的生成对抗网络的连续训练
Successive Training of a Generative Adversarial Network for the Design of an Optical Cloak
摘要：
提出了一种基于深度卷积生成对抗网络（DCGAN）的二维光学隐身衣优化设计算法。光学隐身由均匀各向同性电介质材料制成的外壳组成，隐身是通过外壳的几何形状实现的。我们使用来自DCGAN的解决方案的反馈回路来连续地重新训练它，并提高其预测和找到最佳几何形状的能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
