Deep learning based pulse prediction of nonlinear dynamics in fiber optics

Abstract

The initial state of a nonlinear optical fiber system plays a vital role in the ultrafast pulse evolution dynamic. In this work, a data-driven compressed convolutional neural network, named inverse network, is proposed to predict initial pulse distribution through a series of discrete power profiles at different propagation distances. The inverse network is trained and tested based on two typical nonlinear dynamics: (1) the pulse evolution in a fiber optical parametric amplifier system and (2) soliton pair evolution in high-nonlinear fibers. Great prediction accuracy is reached when the epoch grows to 5000 in both cases, with the normalized root mean square errors below 0.01 on the entire testing set. Meanwhile, the lightweight network is highly effective. In this work, it takes approximately 30 seconds for 5,000 epochs training with a dataset size of 900. The inverse network is further tested and analyzed on the dataset with different signal-to-noise ratios and input sizes. The results show fair stability at the deviation on the testing set. The proposed inverse network demonstrates a promising approach to optimizing the initial pulse of fiber optics systems.

Predicting nonlinear reshaping of periodic signals in optical fibre with a neural network

“…2(a). It is worth noting that recently, many works have explored the application of more advanced ML methods to NLSE-related problems, such as recurrent [5,6,20], convolutional [21][22][23][24], or physics-informed [25][26][27][28] NNs, achieving remarkable performance. In this work, our goal is not to showcase the most powerful ML method for solving the NLSE propagation problem being considered but rather to emphasise that an easily accessible and model-free method [29] can already fit our purpose well.…”

Artificial Intelligence-Enabled Mode-Locked Fiber Laser: A Review

“…Yang et al [63] proposed a convolutional feature separation modeling method with low complexity and strong, highly accurate generalization ability. Sui et al [64] proposed a compressed CNN to accurately predict the initial pulse distribution of the nonlinear optical fiber system with different initial widths and powers. Gautam et al [65] used a CNN model based on knowledge distillation to learn the pulse evolution in the nonlinear fiber; this model uses few trainable parameters to obtain good generalization and a fast convergence rate.…”

Fiber laser development enabled by machine learning: review and prospect

“…There is a strong interest in finding a datadriven solution through machine learning. In recent years, machine learning has shown power in predicting complex nonlinear evolution governed by NLSE [73][74][75]. PINNs guided with specific theories can also be an effective analytical tool to solve PDEs from incomplete models and limited data [76].…”

“…Hao Sui et al demonstrated a compressed convolutional neural network as an inverse computation tool to predict initial pulse distribution from a series of discrete power profiles at different propagation distances [75]. Two nonlinear dynamics, the pulse evolution in fiber optical parametric amplifier systems and the soliton pair evolution in high-nonlinear fiber, are studied in simulations.…”