论文标题
通过深度强化学习的自动化光学多层设计
Automated Optical Multi-layer Design via Deep Reinforcement Learning
论文作者
论文摘要
光学多层薄膜广泛用于需要光子设计的光学和能量应用中。工程师经常根据其物理直觉设计这样的结构。但是，仅依靠人类专家可能很耗时，并且可能导致次优的设计，尤其是在设计空间很大的时候。在这项工作中，我们将多层光学设计任务构架为序列生成问题。提出了一个深层生成网络，以有效地生成光学层序列。我们使用近端策略优化训练深层生成网络，以生成具有所需属性的多层结构。所提出的方法应用于两个能源应用。我们的算法成功地发现了高性能设计，胜过任务1的人类专家设计的结构以及任务2中最先进的模因算法。
Optical multi-layer thin films are widely used in optical and energy applications requiring photonic designs. Engineers often design such structures based on their physical intuition. However, solely relying on human experts can be time-consuming and may lead to sub-optimal designs, especially when the design space is large. In this work, we frame the multi-layer optical design task as a sequence generation problem. A deep sequence generation network is proposed for efficiently generating optical layer sequences. We train the deep sequence generation network with proximal policy optimization to generate multi-layer structures with desired properties. The proposed method is applied to two energy applications. Our algorithm successfully discovered high-performance designs, outperforming structures designed by human experts in task 1, and a state-of-the-art memetic algorithm in task 2.
