[HTML][HTML] Reusability report: Predicting spatiotemporal nonlinear dynamics in multimode fibre optics with a recurrent neural network
Results Temporal results. The datasets generated by the aforementioned TD-BPM contain
1,000 examples of spatiotemporal nonlinear propagation of femtosecond pulses (see …
1,000 examples of spatiotemporal nonlinear propagation of femtosecond pulses (see …
Physics‐Informed Neural Network for Nonlinear Dynamics in Fiber Optics
A physics‐informed neural network (PINN) that combines deep learning with physics is
studied to solve the nonlinear Schrödinger equation for learning nonlinear dynamics in fiber …
studied to solve the nonlinear Schrödinger equation for learning nonlinear dynamics in fiber …
[HTML][HTML] Controlling spatiotemporal nonlinearities in multimode fibers with deep neural networks
Spatiotemporal nonlinear interactions in multimode fibers are of interest for beam shaping
and frequency conversion by exploiting the nonlinear interaction of different pump modes …
and frequency conversion by exploiting the nonlinear interaction of different pump modes …
Deep neural network for modeling soliton dynamics in the mode-locked laser
Integrating the information of the first cycle of an optical pulse in a cavity into the input of a
neural network, a bidirectional long short-term memory (Bi_LSTM) recurrent neural network …
neural network, a bidirectional long short-term memory (Bi_LSTM) recurrent neural network …
Machine learning analysis of instabilities in noise-like pulse lasers
Neural networks have been recently shown to be highly effective in predicting time-domain
properties of optical fiber instabilities based only on analyzing spectral intensity profiles …
properties of optical fiber instabilities based only on analyzing spectral intensity profiles …
Numerical Prediction of Incoherent Modulation Instability Dynamics through Deep Learning Approaches
In nonlinear fiber optics, Modulation Instability (MI) is characterized by the emergence of
new spectral components, either seeded from optical signals or appearing spontaneously …
new spectral components, either seeded from optical signals or appearing spontaneously …
Machine learning for ultrafast nonlinear fibre photonics
C Finot, S Boscolo, J Peng, A Ermolaev… - … on Transparent Optical …, 2024 - hal.science
We provide an overview of our latest advances in the application of machine learning
methods to ultrafast nonlinear fibre optics. We establish that neural networks are capable of …
methods to ultrafast nonlinear fibre optics. We establish that neural networks are capable of …
Convolutional neural network reconstruction of ultrashort optical pulses
Ultrashort pulse characterization and measurement is critical in the field of ultrafast and
nonlinear optics. Here we present a method to reconstruct the complex pulse profile using a …
nonlinear optics. Here we present a method to reconstruct the complex pulse profile using a …
A prince for the sleeping beauty-NFT for soliton signal processing
The soliton, the eigen-solution of nonlinear Schrodinger equation (NLSE), has been
investigated in many branches of physics. The first prediction of a soliton in optical fiber …
investigated in many branches of physics. The first prediction of a soliton in optical fiber …
Neural Networks for predicting optical pulse propagation through highly nonlinear fibers
Due to increase in demand of the optical fiber communication system there is a special
emphasis on diagnosing ultrashort pulses. The linear and nonlinear distortions introduced …
emphasis on diagnosing ultrashort pulses. The linear and nonlinear distortions introduced …