Rapid prediction of complex nonlinear dynamics in Kerr resonators using the recurrent neural network.

Tianye Huang, Lin Chen, Mingkong Lu, Jianxing Pan, Chaoyu Xu, Pei Wang, Perry Ping Shum

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Article in Frontiers of optoelectronics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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7 authors.

Tianye HuangSchool of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan), Wuhan, 430074, China. huangty@cug.edu.cn.

Lin ChenSchool of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan), Wuhan, 430074, China.

Mingkong LuYazheng Technology Group Co., Ltd, Wuhan, 430200, China.

Jianxing PanSchool of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan), Wuhan, 430074, China.

Chaoyu XuSchool of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan), Wuhan, 430074, China.

Pei WangWuhan Huaray Precision Laser Co., Ltd, Wuhan, 430223, China.

Perry Ping ShumDepartment of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.

Funding

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6 · The paper itself

Abstract

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.

Indexed as

Cavity soliton (CS)DynamicsKerr ring resonatorsNonlinearRecurrent neural network (RNN)

Identifiers

PMID40996580

DOI10.1007/s12200-025-00164-4

PMCPMC12463783

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