Hanzhang Pei, M. Whittlesey, Qiang Du, A. Galvanauskas
{"title":"相干脉冲叠加放大作为深度递归神经网络的设计与运行","authors":"Hanzhang Pei, M. Whittlesey, Qiang Du, A. Galvanauskas","doi":"10.1364/cleo_si.2021.stu2e.2","DOIUrl":null,"url":null,"abstract":"We show equivalence of coherent pulse stacking system to a deep recurrent neural network, and experimentally demonstrate real-time learning on stacking cavities and input pulses, necessary for high fidelity coherent temporal combining with ∼102 pulses.","PeriodicalId":384075,"journal":{"name":"2021 Conference on Lasers and Electro-Optics (CLEO)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Design and Operation of Coherent Pulse Stacking Amplification as a Deep Recurrent Neural Network\",\"authors\":\"Hanzhang Pei, M. Whittlesey, Qiang Du, A. Galvanauskas\",\"doi\":\"10.1364/cleo_si.2021.stu2e.2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We show equivalence of coherent pulse stacking system to a deep recurrent neural network, and experimentally demonstrate real-time learning on stacking cavities and input pulses, necessary for high fidelity coherent temporal combining with ∼102 pulses.\",\"PeriodicalId\":384075,\"journal\":{\"name\":\"2021 Conference on Lasers and Electro-Optics (CLEO)\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 Conference on Lasers and Electro-Optics (CLEO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1364/cleo_si.2021.stu2e.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":"2021 Conference on Lasers and Electro-Optics (CLEO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1364/cleo_si.2021.stu2e.2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Design and Operation of Coherent Pulse Stacking Amplification as a Deep Recurrent Neural Network
We show equivalence of coherent pulse stacking system to a deep recurrent neural network, and experimentally demonstrate real-time learning on stacking cavities and input pulses, necessary for high fidelity coherent temporal combining with ∼102 pulses.