计算机科学
灵活性(工程)
独特性
人工神经网络
反问题
反向
网络规划与设计
过程(计算)
航程(航空)
采样(信号处理)
人工智能
机器学习
算法
材料科学
数学
电信
数学分析
统计
几何学
探测器
复合材料
操作系统
作者
X. Yuan,Leilei Gu,Zichen Wei,Wenfeng Ding,Qiongxiong Ma,Jianping Guo
标识
DOI:10.1016/j.optcom.2024.130296
摘要
The process of machine learning-assisted nanophotonicsinverse design has been plagued by the problem of non-uniqueness for a long time, which is a problem worth studying. In this paper, we present a novel methodology for the design of bowtie optical nanoantennas (BONAs) by employing a Bootstrap Sampling Style Ensemble Neural Network (BSENN) model. Our approach combines a bagging algorithm with a tandem neural network to address the non-uniqueness challenge inherent in the inverse design process of BONAs. By splitting the data, training in batches, and integrating the results, our BSENN model is able to provide reliable predictions and offer a solution to the non-uniqueness problem. The main objective of our work is to explore diverse BONAs design structures that yield identical spectral responses, thereby providing a broader range of alternatives for the design of optical nanoantennas. Through the utilization of the BSENN model, we aim to enhance the design process and offer increased flexibility and versatility in the field of optical nanoantenna design.
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