{"title":"物理神经网络的多平面光转换设计","authors":"Zheyuan Zhu, Joe H. Doerr, Guifang Li, S. Pang","doi":"10.1364/dh.2022.m6a.2","DOIUrl":null,"url":null,"abstract":"We present a physical neural network (PNN) approach towards multiplane light conversion (MPLC) design. PNN performs a full parameter search with flexible optimization pathways and can tune various design attributes as hyperparameters.","PeriodicalId":227456,"journal":{"name":"Digital Holography and 3-D Imaging 2022","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Multiplane light conversion design with physical neural network\",\"authors\":\"Zheyuan Zhu, Joe H. Doerr, Guifang Li, S. Pang\",\"doi\":\"10.1364/dh.2022.m6a.2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a physical neural network (PNN) approach towards multiplane light conversion (MPLC) design. PNN performs a full parameter search with flexible optimization pathways and can tune various design attributes as hyperparameters.\",\"PeriodicalId\":227456,\"journal\":{\"name\":\"Digital Holography and 3-D Imaging 2022\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Digital Holography and 3-D Imaging 2022\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1364/dh.2022.m6a.2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Holography and 3-D Imaging 2022","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1364/dh.2022.m6a.2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multiplane light conversion design with physical neural network
We present a physical neural network (PNN) approach towards multiplane light conversion (MPLC) design. PNN performs a full parameter search with flexible optimization pathways and can tune various design attributes as hyperparameters.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
