人工神经网络用于光子学应用：从算法到实现
Artificial Neural Networks for Photonic Applications: From Algorithms to Implementation
AI总结：
本文综述人工神经网络在光子学中的应用，梳理模型设计、微调、硬件实现、复杂度评估与压缩方法，并展示其在光通信、成像、传感和材料激光器设计中的价值。
摘要：
这篇关于人工神经网络在光子学中应用的教程式综述面向广泛读者，涵盖光学研究与工程界以及计算机科学和应用数学界。我们在此聚焦这些学科交叉处的研究领域，力求在各领域特有的技术细节与整体清晰度之间取得适当平衡。首先，我们简要回顾若干核心神经网络类型的关键属性与特点，我们认为这些类型与光子学最相关，并将层的理论设计与一些光子学硬件实现联系起来。随后，我们阐明如何微调所选模型的设计，使其以优化后的精度完成所需任务。接着，在综述部分，我们讨论神经网络在光子学中若干选定应用的最新发展与进展，包括与光通信、成像、传感以及新材料和激光器设计相关的多个方面。在下一节中，我们特别强调在从算法转向硬件实现的背景下，如何准确评估神经网络的复杂度。所引入的复杂度特征被用于分析神经网络在光通信中的应用，并将其作为一个具体但极其重要的示例，与一些基准信号处理方法进行比较。我们将机器学习中众所周知的模型压缩策略描述，与近期在神经网络光学应用中提出的一些新技术结合起来。需要强调的是，尽管本教程式综述的重点是光子学，但我们相信这里介绍的方法和技术可在更广泛的科学与工程应用中发挥作用。
英文摘要：
This tutorial-review on applications of artificial neural networks in photonics targets a broad audience, ranging from optical research and engineering communities to computer science and applied mathematics. We focus here on the research areas at the interface between these disciplines, attempting to find the right balance between technical details specific to each domain and overall clarity. First, we briefly recall key properties and peculiarities of some core neural network types, which we believe are the most relevant to photonics, also linking the layer's theoretical design to some photonics hardware realizations. After that, we elucidate the question of how to fine-tune the selected model's design to perform the required task with optimized accuracy. Then, in the review part, we discuss recent developments and progress for several selected applications of neural networks in photonics, including multiple aspects relevant to optical communications, imaging, sensing, and the design of new materials and lasers. In the following section, we put a special emphasis on how to accurately evaluate the complexity of neural networks in the context of the transition from algorithms to hardware implementation. The introduced complexity characteristics are used to analyze the applications of neural networks in optical communications, as a specific, albeit highly important example, comparing those with some benchmark signal processing methods. We combine the description of the well-known model compression strategies used in machine learning, with some novel techniques introduced recently in optical applications of neural networks. It is important to stress that although our focus in this tutorial-review is on photonics, we believe that the methods and techniques presented here can be handy in a much wider range of scientific and engineering applications.
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。
