论文标题
纳米 - 光子学的深度学习：逆设计及其他
Deep learning in nano-photonics: inverse design and beyond
论文作者
论文摘要
在纳米光子学的背景下，深度学习主要是根据光子设备或纳米结构的逆设计的潜力来讨论的。许多有关机器学习逆设计的最新作品都是高度特异的，并且相应方法的缺点通常不清楚。因此，在这篇评论中，我们希望对深度学习的能力进行批判性审查以及到目前为止取得的进展。我们在较高级别以及各自应用的背景下对不同的基于深度学习的逆设计方法进行了分类，并严格地讨论了它们的优势和缺点。尽管社区关注的很大一部分在于纳米光子逆设计，但深度学习已发展为多种应用的工具。因此，评论的第二部分将重点放在纳米光子学“超越逆设计”中的机器学习研究上。物理学跨越了神经网络，以大量加速光子学模拟，稀疏数据重建，成像和“知识发现”到实验应用。
Deep learning in the context of nano-photonics is mostly discussed in terms of its potential for inverse design of photonic devices or nanostructures. Many of the recent works on machine-learning inverse design are highly specific, and the drawbacks of the respective approaches are often not immediately clear. In this review we want therefore to provide a critical review on the capabilities of deep learning for inverse design and the progress which has been made so far. We classify the different deep learning-based inverse design approaches at a higher level as well as by the context of their respective applications and critically discuss their strengths and weaknesses. While a significant part of the community's attention lies on nano-photonic inverse design, deep learning has evolved as a tool for a large variety of applications. The second part of the review will focus therefore on machine learning research in nano-photonics "beyond inverse design". This spans from physics informed neural networks for tremendous acceleration of photonics simulations, over sparse data reconstruction, imaging and "knowledge discovery" to experimental applications.
