光子学
贝叶斯优化
计算机科学
强化学习
背景(考古学)
粒子群优化
量子
人工智能
算法
物理
量子力学
古生物学
光学
生物
作者
María José Sánchez,Christer Everly,P. A. Postigo
出处
期刊:Journal of The Optical Society of America B-optical Physics
[Optica Publishing Group]
日期:2024-01-08
卷期号:41 (2): A177-A177
被引量:4
摘要
The development and optimization of photonic devices and various other nanostructure electromagnetic devices present a computationally intensive task. Much optimization relies on finite-difference time-domain or finite element analysis simulations, which can become very computationally demanding for finely detailed structures and dramatically reduce the available optimization space. In recent years, various inverse design machine learning (ML) techniques have been successfully applied to realize previously unexplored optimization spaces for photonic and quantum photonic devices. In this review, recent results using conventional optimization methods, such as the adjoint method and particle swarm, are examined along with ML optimization using convolutional neural networks, Bayesian optimizations with deep learning, and reinforcement learning in the context of new applications to photonics and quantum photonics.
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