arXiv subjects

Chao-Qing Dai

Publications and source records attributed to Chao-Qing Dai.

7 recordsLinked to original sources

arXiv2024

Polarization induced buildup and switching mechanisms for soliton molecules composed of noise like pulse transition states

Buildup and switching mechanisms of solitons in complex nonlinear systems are fundamentally important dynamical regimes. Using a novel strongly nonlinear optical system,the work reveals a new buildup scenario for soliton molecules , which includes a long-duration stage dominated by the emergence of transient NLPs modes to withstand strong disturbances arising from turbulence and extreme nonlinearity in the optical cavity. Systematic simulations reveal effects of the PC rotation angle and intra-cavity nonlinearity on the periodic phase transitions between the different soliton states, and accurately reproduce the experimentally observed buildup and switching mechanisms. These findings could enhance our fundamental study and points to potential uses in designing information encoding systems.

physics.optics↗

arXiv2024

Deep learning for dynamic modeling and coded information storage of vector-soliton pulsations in mode-locked fiber lasers

Soliton pulsations are ubiquitous feature of non-stationary soliton dynamics in mode-locked lasers and many other physical systems. To overcome difficulties related to huge amount of necessary computations and low efficiency of traditional numerical methods in modeling the evolution of non-stationary solitons, we propose a two-parallel bidirectional long short-term memory recurrent neural network, with the main objective to predict dynamics of vector-soliton pulsations in various complex states, whose real-time dynamics is verified by experiments. Besides, the scheme of coded information storage based on the TP-Bi_LSTM RNN, instead of actual pulse signals, is realized too. The findings offer new applications of deep learning to ultrafast optics and information storage.

physics.optics↗

arXiv2022

Predicting nonlinear dynamics of optical solitons in optical fiber via the SCPINN

The strongly-constrained physics-informed neural network (SCPINN) is proposed by adding the information of compound derivative embedded into the soft-constraint of physics-informed neural network(PINN). It is used to predict nonlinear dynamics and the formation process of bright and dark picosecond optical solitons, and femtosecond soliton molecule in the single-mode fiber, and reveal the variation of physical quantities including the energy, amplitude, spectrum and phase of pulses during the soliton transmission. The adaptive weight is introduced to accelerate the convergence of loss function in this new neural network. Compared with the PINN, the accuracy of SCPINN in predicting soliton dynamics is improved by 5-11 times. Therefore, the SCPINN is a forward-looking method to study the modeling and analysis of soliton dynamics in the fiber.

nlin.PS↗

arXiv2022

Data-driven soliton solutions and model parameters of nonlinear wave models via the conservation-law constrained neural network method

In the process of the deep learning, we integrate more integrable information of nonlinear wave models, such as the conservation law obtained from the integrable theory, into the neural network structure, and propose a conservation-law constrained neural network method with the flexible learning rate to predict solutions and parameters of nonlinear wave models. As some examples, we study real and complex typical nonlinear wave models, including nonlinear Schrödinger equation, Korteweg-de Vries and modified Korteweg-de Vries equations. Compared with the traditional physics-informed neural network method, this new method can more accurately predict solutions and parameters of some specific nonlinear wave models even when less information is needed, for example, in the absence of the boundary conditions. This provides a reference to further study solutions of nonlinear wave models by combining the deep learning and the integrable theory.

nlin.PS↗

arXiv2021

Predicting the dynamic process and model parameters of the vector optical solitons in birefringent fibers via the modified PINN

A modified physics-informed neural network is used to predict the dynamics of optical pulses including one-soliton, two-soliton, and rogue wave based on the coupled nonlinear Schrödinger equation in birefringent fibers. At the same time, the elastic collision process of the mixed bright-dark soliton is predicted. Compared the predicted results with the exact solution, the modified physics-informed neural network method is proven to be effective to solve the coupled nonlinear Schrödinger equation. Moreover, the dispersion coefficients and nonlinearity coefficients of the coupled nonlinear Schrodinger equation can be learned by modified physics-informed neural network. This provides a reference for us to use deep learning methods to study the dynamic characteristics of solitons in optical fibers.

nlin.PS↗

arXiv2021

Modified physics-informed neural network method based on the conservation law constraint and its prediction of optical solitons

Based on conservation laws as one of the important integrable properties of nonlinear physical models, we design a modified physics-informed neural network method based on the conservation law constraint. From a global perspective, this method imposes physical constraints on the solution of nonlinear physical models by introducing the conservation law into the mean square error of the loss function to train the neural network. Using this method, we mainly study the standard nonlinear Schrödinger equation and predict various data-driven optical soliton solutions, including one-soliton, soliton molecules, two-soliton interaction, and rogue wave. In addition, based on various exact solutions, we use the modified physics-informed neural network method based on the conservation law constraint to predict the dispersion and nonlinear coefficients of the standard nonlinear Schrödinger equation. Compared with the traditional physics-informed neural network method, the modified method can significantly improve the calculation accuracy.

nlin.PS↗

arXiv2021

Data-driven femtosecond optical soliton excitations and parameters discovery of the high-order NLSE using the PINN

We use the physics-informed neural network to solve a variety of femtosecond optical soliton solutions of the high order nonlinear Schrödinger equation, including one-soliton solution, two-soliton solution, rogue wave solution, W-soliton solution and M-soliton solution. The prediction error for one-soliton, W-soliton and M-soliton is smaller. As the prediction distance increases, the prediction error will gradually increase. The unknown physical parameters of the high order nonlinear Schrödinger equation are studied by using rogue wave solutions as data sets. The neural network is optimized from three aspects including the number of layers of the neural network, the number of neurons, and the sampling points. Compared with previous research, our error is greatly reduced. This is not a replacement for the traditional numerical method, but hopefully to open up new ideas.

nlin.PS↗