Rogue hunters: A dual-branch deep learning framework with regional insight and gradient-enhanced loss for optical rogue wave prediction

Qibo Xu, Longnv Huang, Jian Yang and Hua Yang

Chaos, Solitons & Fractals, 2025, vol. 200, issue P1

Abstract: This paper introduces a dual-branch neural network for real-time prediction of optical rogue waves, which are complex nonlinear phenomena in fiber-optic supercontinuum generation. Our framework employs a threshold-based segmentation strategy to partition optical field data into high-intensity and low-intensity regions, allowing for specialized processing of extreme events and background components. The high-intensity branch utilizes a network of depthwise separable convolutions and a bidirectional Long Short-Term Memory (LSTM) with an attention mechanism, while the low-intensity branch uses a simplified LSTM-attention architecture. Simulation results demonstrate that our model accurately reproduces key dynamical features, such as soliton formation and energy exchange. A comparative analysis shows that the dual-branch strategy achieves a temporal prediction error of 0.00641, reducing the error by approximately 70.7% compared to a conventional holistic model, while also decreasing training time by 16.7%. These results highlight the framework’s potential for real-time monitoring and design of high-power nonlinear optical systems.

Keywords: Optical rogue waves; Dual-branch neural network; Regional segmentation; Gradient-enhanced loss; Nonlinear optical phenomenon (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077925009683
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:200:y:2025:i:p1:s0960077925009683

DOI: 10.1016/j.chaos.2025.116955

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().