Low-complexity full-field ultrafast nonlinear dynamics prediction by a convolutional feature separation modeling method

@article{Yang2022LowcomplexityFU,
  title={Low-complexity full-field ultrafast nonlinear dynamics prediction by a convolutional feature separation modeling method},
  author={Hang Yang and Haocheng Zhao and Zekun Niu and Guoqing Pu and Shilin Xiao and Weisheng Hu and Lilin Yi},
  journal={Optics Express},
  year={2022}
}
, Abstract: The modeling and prediction of the ultrafast nonlinear dynamics in the optical fiber are essential for the studies of laser design, experimental optimization, and other fundamental applications. The traditional propagation modeling method based on the nonlinear Schrödinger equation (NLSE) has long been regarded as extremely time-consuming, especially for designing and optimizing experiments. The recurrent neural network (RNN) has been implemented as an accurate intensity prediction… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 28 REFERENCES

Predicting ultrafast nonlinear dynamics in fibre optics with a recurrent neural network

This work shows how a recurrent neural network with long short-term memory accurately predicts the temporal and spectral evolution of higher-order soliton compression and supercontinuum generation, solely from a given transform-limited input pulse intensity profile.

Physics‐Informed Neural Network for Nonlinear Dynamics in Fiber Optics

The results report here show that the PINN is not only an effective partial differential equation solver, but also a prospective technique to advance the scientific computing and automatic modeling in fiber optics.

Feed-forward neural network as nonlinear dynamics integrator for supercontinuum generation.

The nonlinear propagation of ultrashort pulses in optical fibers depends sensitively on the input pulse and fiber parameters. As a result, the optimization of propagation for specific applications

Machine learning and applications in ultrafast photonics

A number of specific areas where the promise of machine learning in ultrafast photonics has already been realized are highlighted, including the design and operation of pulsed lasers, and the characterization and control of ultrafast propagation dynamics.

Intelligent programmable mode-locked fiber laser with a human-like algorithm

This work experimentally demonstrates the first intelligent programmable mode-locked fiber laser enabled by the proposed human-like algorithm, combining the human approach with machine speed, computing capability, and precision.

Universality of the Peregrine Soliton in the Focusing Dynamics of the Cubic Nonlinear Schrödinger Equation.

Experimental confirmation of the universal emergence of the Peregrine soliton predicted to occur during pulse propagation in the semiclassical limit of the focusing nonlinear Schrödinger equation and measurements of temporal focusing of high power pulses reveal both intensity and phase signatures of thePeregrinesoliton during the initial nonlinear evolution stage are reported.

Reduced Complexity Digital Back-Propagation Methods for Optical Communication Systems

Two proposals to reduce the hardware complexity required by digital back-propagation are discussed, one confirms and extends published results for non-dispersion managed link, while the second introduces a novel method applicable to dispersion managed links, showing complexity reductions in the order of 50% and up to 85%, respectively.

Self-steepening of light pulses.

The self-steepening, or change in shape, of light pulses due to propagation in a medium with an intensity-dependent index of refraction is investigated. The time required for the pulse to steepen