光子学
计算机科学
深度学习
背景(考古学)
人工智能
反问题
物理
数学
光学
生物
数学分析
古生物学
作者
Peter R. Wiecha,Arnaud Arbouet,Christian Girard,Otto L. Muskens
出处
期刊:Photonics Research
[The Optical Society]
日期:2021-04-14
卷期号:9 (5): B182-B182
被引量:217
摘要
Deep learning in the context of nano-photonics is mostly discussed in terms of its potential for inverse design of photonic devices or nano-structures. 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.
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