{"title":"利用机器学习模型高效预测六方 PCF 的光学特性","authors":"","doi":"10.1016/j.ijleo.2024.171929","DOIUrl":null,"url":null,"abstract":"<div><p>This research explores the use of machine learning (ML) models to forecast optical characteristics in photonic crystal fibers (PCF). Specifically, we focus on a solid core index-guided PCF with a hexagonal cladding arrangement. The primary challenges to PCF propagation analysis and predictions are accuracy, computational error, and time constraints. To address these difficulties, we have specially used ML ensemble models including Decision Tree Regressor (DTR), Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), eXtreme Gradient Boosting Regression (XGBR), and Bagging Regressor (BR). Model performance is assessed using metrics like Mean Squared Error (MSE) and R-squared (R2) through 10-fold cross-validation. Our key findings show that the GBR model outperforms other models and shows extremely low MSE and outstanding R2 values in predicting effective refractive index (Neff), effective mode area (Aeff), confinement loss, and dispersion. In addition, the study compares the performance of ML models with that of previous works using Artificial Neural Network (ANN), demonstrating improved efficiency in predicting optical characteristics for hexagonal PCFs.</p></div>","PeriodicalId":19513,"journal":{"name":"Optik","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Efficient prediction of optical properties in hexagonal PCF using machine learning models\",\"authors\":\"\",\"doi\":\"10.1016/j.ijleo.2024.171929\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This research explores the use of machine learning (ML) models to forecast optical characteristics in photonic crystal fibers (PCF). Specifically, we focus on a solid core index-guided PCF with a hexagonal cladding arrangement. The primary challenges to PCF propagation analysis and predictions are accuracy, computational error, and time constraints. To address these difficulties, we have specially used ML ensemble models including Decision Tree Regressor (DTR), Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), eXtreme Gradient Boosting Regression (XGBR), and Bagging Regressor (BR). Model performance is assessed using metrics like Mean Squared Error (MSE) and R-squared (R2) through 10-fold cross-validation. Our key findings show that the GBR model outperforms other models and shows extremely low MSE and outstanding R2 values in predicting effective refractive index (Neff), effective mode area (Aeff), confinement loss, and dispersion. In addition, the study compares the performance of ML models with that of previous works using Artificial Neural Network (ANN), demonstrating improved efficiency in predicting optical characteristics for hexagonal PCFs.</p></div>\",\"PeriodicalId\":19513,\"journal\":{\"name\":\"Optik\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Optik\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0030402624003280\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optik","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0030402624003280","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Engineering","Score":null,"Total":0}
Efficient prediction of optical properties in hexagonal PCF using machine learning models
This research explores the use of machine learning (ML) models to forecast optical characteristics in photonic crystal fibers (PCF). Specifically, we focus on a solid core index-guided PCF with a hexagonal cladding arrangement. The primary challenges to PCF propagation analysis and predictions are accuracy, computational error, and time constraints. To address these difficulties, we have specially used ML ensemble models including Decision Tree Regressor (DTR), Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), eXtreme Gradient Boosting Regression (XGBR), and Bagging Regressor (BR). Model performance is assessed using metrics like Mean Squared Error (MSE) and R-squared (R2) through 10-fold cross-validation. Our key findings show that the GBR model outperforms other models and shows extremely low MSE and outstanding R2 values in predicting effective refractive index (Neff), effective mode area (Aeff), confinement loss, and dispersion. In addition, the study compares the performance of ML models with that of previous works using Artificial Neural Network (ANN), demonstrating improved efficiency in predicting optical characteristics for hexagonal PCFs.
期刊介绍:
Optik publishes articles on all subjects related to light and electron optics and offers a survey on the state of research and technical development within the following fields:
Optics:
-Optics design, geometrical and beam optics, wave optics-
Optical and micro-optical components, diffractive optics, devices and systems-
Photoelectric and optoelectronic devices-
Optical properties of materials, nonlinear optics, wave propagation and transmission in homogeneous and inhomogeneous materials-
Information optics, image formation and processing, holographic techniques, microscopes and spectrometer techniques, and image analysis-
Optical testing and measuring techniques-
Optical communication and computing-
Physiological optics-
As well as other related topics.