An illustrated tutorial on global optimization in nanophotonics
An illustrated tutorial on global optimization in nanophotonics
复制标题
纳米光子学全局优化图解教程
DOI：
10.1364/josab.506389
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
A. Moreau
中科院分区：
文献类型：
--
作者：
Paul M. Bennet;Denis Langevin;Chaymae Essoual;Abdourahman Khaireh;O. Teytaud;Peter Wiecha;A. Moreau
光子结构的逆向设计的数值优化是一种工具，它提供了越来越令人信服的结果-即使光子学问题的波动性质使它们特别复杂。与此同时，全局优化领域正在迅速发展，但容易出现重复性问题，这使得更难确定要使用的正确算法。本文是光子学问题全局优化的一个教程。我们提供了一个整体的优化算法和严格的方法，有兴趣使用这些工具的物理学家的一般背景-特别是在逆设计的背景下。我们建议的算法，并提供其效率的解释。我们提供的代码和示例可以在线运行，集成了快速模拟代码和Nevergrad，一个最先进的基准库。最后，我们展示了如何物理直觉可以用来讨论优化结果，并确定解决方案是否令人满意。
Numerical optimization for the inverse design of photonic structures is a tool which is providing increasingly convincing results -- even though the wave nature of problems in photonics makes them particularly complex. In the meantime, the field of global optimization is rapidly evolving but is prone to reproducibility problems, making it harder to identify the right algorithms to use. This paper is thought as a tutorial on global optimization for photonic problems. We provide a general background on global optimization algorithms and a rigorous methodology for a physicist interested in using these tools -- especially in the context of inverse design. We suggest algorithms and provide explanations for their efficiency. We provide codes and examples as an illustration than can be run online, integrating quick simulation code and Nevergrad, a state-of-the-art benchmarking library. Finally, we show how physical intuition can be used to discuss optimization results and to determine whether the solutions are satisfactory or not.
概述：深度学习方法的理论特性
DOI：
--
发表时间：
2019
期刊：
影响因子：
--
作者：
大塚友貴;福嶋慶繁;Kazushi Ikeda
通讯作者：
Kazushi Ikeda