Modelling self-similar parabolic pulses in optical fibres with a neural network

2020/12/02 by Sonia Boscolo, Boscolo, Sonia, John M. Dudley +3 · 1 citation
Engineering · Physics and Astronomy · #Advanced Fiber Laser Technologies #Artificial intelligence #Artificial neural network #Backpropagation #Computer science #Control engineering #Engineering #FOS: Physical sciences #Feed forward #Feedforward neural network #Focus (optics) #Laser #Laser beams #Nonlinear system #Optical Network Technologies #Optics #Optics (physics.optics) #Photonic Crystal and Fiber Optics #Physics #Pulse (music) #Self-focusing #physics.optics

paper · pdf · open access · doi:10.48550/arxiv.2012.01092

published in Figshare (Figshare (United Kingdom)) · arXiv admin note: text overlap with arXiv:2002.08815

arxiv created 2020/12/02 · arxiv updated 2020/12/03 · openalex publication_date 2023/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

We expand our previous analysis of nonlinear pulse shaping in optical fibres using machine learning [Opt. Laser Technol., 131 (2020) 106439] to the case of pulse propagation in the presence of gain/loss, with a special focus on the generation of self-similar parabolic pulses. We use a supervised feedforward neural network paradigm to solve the direct and inverse problems relating to the pulse shaping, bypassing the need for direct numerical solution of the governing propagation model.

Citations

• Artificial neural networks for nonlinear pulse shaping in optical fibers
• Machine learning and the physical sciences

Cited by

• Transfer Learning for Transformer-Based Modeling of Nonlinear Pulse Evolution in Er-Doped Fiber Amplifiers

Related

• Predicting ultrafast nonlinear dynamics in fibre optics with a recurrent neural network
• Transfer Learning for Transformer-Based Modeling of Nonlinear Pulse Evolution in Er-Doped Fiber Amplifiers
• Introducing γ-lifting for Learning Nonlinear Pulse Shaping in Coherent Optical Communication
• Programming Nonlinear Propagation for Efficient Optical Learning Machines
• Physics-informed Neural Network for Nonlinear Dynamics in Fiber Optics