用于光子器件评估和设计的深度神经网络
Deep neural networks for the evaluation and design of photonic devices
摘要：
数据科学革命有望改变光子系统的模拟和设计方式。光子学在许多方面都是机器学习的理想基础：大部分计算电磁学的目标是捕捉高维空间中的非线性关系，这是神经网络的核心力量。此外，Maxwell解算器的主流可用性使得神经网络的训练和评估可以广泛地访问并针对特定问题进行定制。在这篇综述中，我们将展示配置为判别网络的深度神经网络如何从训练集学习，并作为高速代理电磁求解器运行。我们还将研究深度生成网络如何学习设备分布中的几何特征，甚至配置为强大的全局优化器。还将讨论光子学背景下的基础数据科学概念，包括网络训练过程，不同网络类和架构的划分以及降维。
英文摘要：
The data sciences revolution is poised to transform the way photonic systems are simulated and designed. Photonics are in many ways an ideal substrate for machine learning: the objective of much of computational electromagnetics is the capture of non-linear relationships in high dimensional spaces, which is the core strength of neural networks. Additionally, the mainstream availability of Maxwell solvers makes the training and evaluation of neural networks broadly accessible and tailorable to specific problems. In this Review, we will show how deep neural networks, configured as discriminative networks, can learn from training sets and operate as high-speed surrogate electromagnetic solvers. We will also examine how deep generative networks can learn geometric features in device distributions and even be configured to serve as robust global optimizers. Fundamental data sciences concepts framed within the context of photonics will also be discussed, including the network training process, delineation of different network classes and architectures, and dimensionality reduction.
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