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

Area of Science:

• Nonlinear optics
• Computational physics
• Artificial intelligence in science

Background:

• Kerr resonators are key for generating optical frequency combs and temporal cavity solitons.
• Traditional numerical simulations of Kerr resonator dynamics, using the Lugiato-Lefever equation (LLE) and split-step Fourier method (SSFM), are computationally intensive.

Purpose of the Study:

• To develop an efficient and accurate method for predicting soliton dynamics in Kerr resonators.
• To overcome the computational limitations of traditional numerical simulations.

Main Methods:

• Proposed a recurrent neural network (RNN) model incorporating prior information feedback.
• Utilized graphics processing units (GPUs) for computational acceleration.
• Compared various RNN architectures, identifying the gated recurrent unit (GRU) as optimal.

Main Results:

• Achieved a 20-fold improvement in computational efficiency compared to traditional methods.
• Demonstrated accurate prediction of soliton dynamics in Kerr resonators.
• The GRU network showed superior performance for this specific task.

Conclusions:

• Artificial intelligence (AI), particularly GRU networks, offers a powerful tool for modeling nonlinear optical dynamics.
• This AI-driven approach accelerates the design of optical frequency combs and the generation of ultrafast pulses.
• Highlights the potential of AI in advancing optical technologies.