{"title":"基于深度学习的飞秒弱种子脉冲条件下中红外超连续的流氓波分析","authors":"","doi":"10.1016/j.chaos.2024.115575","DOIUrl":null,"url":null,"abstract":"<div><div>The generation process of rogue wave (RW) is affected by noise, which is an unstable state, and the existence of RW will reduce the stability of mid-infrared supercontinuum. However, the process of studying RW requires a large amount of data simulation and statistics, and traditional methods are time-consuming and inefficient. Therefore, this paper adopts long short-term memory (LSTM) neural network to obtain the spectrum information after transmission for a certain distance according to the waveform information of the incident pulse. The results show that the LSTM neural network structure can train and predict the peak power, time deviation information, time intensity evolution and spectrum evolution of RW after 10 cm propagation with only changing the number of internal units. And it performs well on both large and small data sets.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analysis of rogue wave in the mid-infrared supercontinuum under femtosecond weak seed pulse conditions based on deep learning\",\"authors\":\"\",\"doi\":\"10.1016/j.chaos.2024.115575\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The generation process of rogue wave (RW) is affected by noise, which is an unstable state, and the existence of RW will reduce the stability of mid-infrared supercontinuum. However, the process of studying RW requires a large amount of data simulation and statistics, and traditional methods are time-consuming and inefficient. Therefore, this paper adopts long short-term memory (LSTM) neural network to obtain the spectrum information after transmission for a certain distance according to the waveform information of the incident pulse. The results show that the LSTM neural network structure can train and predict the peak power, time deviation information, time intensity evolution and spectrum evolution of RW after 10 cm propagation with only changing the number of internal units. And it performs well on both large and small data sets.</div></div>\",\"PeriodicalId\":9764,\"journal\":{\"name\":\"Chaos Solitons & Fractals\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chaos Solitons & Fractals\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0960077924011275\",\"RegionNum\":1,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chaos Solitons & Fractals","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0960077924011275","RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
摘要
流氓波(RW)的产生过程受噪声影响,是一种不稳定状态,流氓波的存在会降低中红外超连续的稳定性。然而,研究流氓波的过程需要大量的数据模拟和统计,传统方法耗时长、效率低。因此,本文采用长短期记忆(LSTM)神经网络,根据入射脉冲的波形信息,获取一定距离传输后的频谱信息。结果表明,LSTM 神经网络结构只需改变内部单元的数量,就能训练和预测 RW 传播 10 cm 后的峰值功率、时间偏差信息、时间强度演变和频谱演变。而且它在大型和小型数据集上都表现良好。
Analysis of rogue wave in the mid-infrared supercontinuum under femtosecond weak seed pulse conditions based on deep learning
The generation process of rogue wave (RW) is affected by noise, which is an unstable state, and the existence of RW will reduce the stability of mid-infrared supercontinuum. However, the process of studying RW requires a large amount of data simulation and statistics, and traditional methods are time-consuming and inefficient. Therefore, this paper adopts long short-term memory (LSTM) neural network to obtain the spectrum information after transmission for a certain distance according to the waveform information of the incident pulse. The results show that the LSTM neural network structure can train and predict the peak power, time deviation information, time intensity evolution and spectrum evolution of RW after 10 cm propagation with only changing the number of internal units. And it performs well on both large and small data sets.
期刊介绍:
Chaos, Solitons & Fractals strives to establish itself as a premier journal in the interdisciplinary realm of Nonlinear Science, Non-equilibrium, and Complex Phenomena. It welcomes submissions covering a broad spectrum of topics within this field, including dynamics, non-equilibrium processes in physics, chemistry, and geophysics, complex matter and networks, mathematical models, computational biology, applications to quantum and mesoscopic phenomena, fluctuations and random processes, self-organization, and social phenomena.