基于递归神经网络的克尔谐振器复杂非线性动力学快速预测。

IF 5.2 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Tianye Huang, Lin Chen, Mingkong Lu, Jianxing Pan, Chaoyu Xu, Pei Wang, Perry Ping Shum
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引用次数: 0

摘要

克尔谐振器是产生光频率梳和时间腔孤子的最常用平台之一。作为研究克尔谐振器非线性动力学的重要方法,传统的数值模拟依赖于采用分步傅立叶方法求解Lugiato-Lefever方程,计算量大,耗时长。为了解决这一挑战,本研究提出了一种具有先验信息反馈的递归神经网络模型,能够有效准确地预测克尔谐振器中的孤子动力学。随着图形处理器(GPU)的加速,计算效率提高了20倍。我们比较了各种递归神经网络,发现门控递归单元(GRU)网络在该任务中表现出优越的性能。本研究突出了人工智能(AI)在克尔谐振器非线性光学动力学建模中的潜力,为设计光频梳和产生超快脉冲铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rapid prediction of complex nonlinear dynamics in Kerr resonators using the recurrent neural network.

Kerr resonator is one of the most popular platforms to produce optical frequency comb and temporal cavity soliton. As an essential method for investigating the nonlinear dynamics of Kerr resonators, traditional numerical simulations rely on solving the Lugiato-Lefever equation (LLE) using the split-step Fourier method (SSFM), which is computationally intensive and time-consuming. To address this challenge, this study proposes a recurrent neural network model with prior information feedback, enabling efficient and accurate prediction of soliton dynamics in Kerr resonator. With the acceleration of graphics processing unit (GPU), the computational efficiency improved by 20 times. We compared various recurrent neural networks and found that the gated recurrent unit (GRU) network demonstrated superior performance in this task. This work highlights the potential of artificial intelligence (AI) for modeling nonlinear optical dynamics in Kerr resonator, paving the way for designing optical frequency comb and generating ultrafast pulse.

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来源期刊
Frontiers of Optoelectronics
Frontiers of Optoelectronics ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
7.80
自引率
0.00%
发文量
583
期刊介绍: Frontiers of Optoelectronics seeks to provide a multidisciplinary forum for a broad mix of peer-reviewed academic papers in order to promote rapid communication and exchange between researchers in China and abroad. It introduces and reflects significant achievements being made in the field of photonics or optoelectronics. The topics include, but are not limited to, semiconductor optoelectronics, nano-photonics, information photonics, energy photonics, ultrafast photonics, biomedical photonics, nonlinear photonics, fiber optics, laser and terahertz technology and intelligent photonics. The journal publishes reviews, research articles, letters, comments, special issues and so on. Frontiers of Optoelectronics especially encourages papers from new emerging and multidisciplinary areas, papers reflecting the international trends of research and development, and on special topics reporting progress made in the field of optoelectronics. All published papers will reflect the original thoughts of researchers and practitioners on basic theories, design and new technology in optoelectronics. Frontiers of Optoelectronics is strictly peer-reviewed and only accepts original submissions in English. It is a fully OA journal and the APCs are covered by Higher Education Press and Huazhong University of Science and Technology. ● Presents the latest developments in optoelectronics and optics ● Emphasizes the latest developments of new optoelectronic materials, devices, systems and applications ● Covers industrial photonics, information photonics, biomedical photonics, energy photonics, laser and terahertz technology, and more
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