芯(光纤)
航程(航空)
材料科学
计算机科学
复合材料
作者
Yordanos Jewani,Michael Petry,Reinaldo Sanchez-Arias,Md. Selim Habib
标识
DOI:10.1364/cleo_at.2024.jw2a.59
摘要
A supervised machine learning framework is implemented to predict the propagation loss of randomly structured nested hollow-core anti-resonant fiber for the first time. The random forest classifier outperforms other methods in accurately predicting the loss.
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