Bidirectional Fourier-Enhanced Deep Operator Network for Spatio-Temporal Propagation in Multi-Mode Fibers
Dinesh Kumar Murugan, Nithyanandan Kanagaraj

TL;DR
This paper introduces a bidirectional operator learning framework that accurately models ultrashort-pulse propagation in multimode fibers, enabling real-time predictions and inverse retrieval with significantly reduced computational cost.
Contribution
It presents one of the first bidirectional operator-learning models for ultrashort-pulse multimode fiber propagation, combining spectral and Fourier features for fast, accurate predictions and inverse solutions.
Findings
Achieves orders-of-magnitude speedup over traditional numerical solvers.
Accurately predicts complex spatio-temporal field evolution in unseen cases.
Enables real-time diagnostics and control of ultrashort pulses.
Abstract
Ultrashort-pulse propagation in graded-index multimode fibers is a highly nonlinear phenomenon driven by several physical processes. Although conventional numerical solvers can reproduce this behavior with high fidelity, their computational cost limits real-time prediction, rapid parameter exploration, experimental feedback, and especially inverse retrieval of input fields from measured outputs. In this work, we introduce an operator learning framework that learns both the forward and inverse propagation operators within a single unified architecture. By combining spectral filters for spatio-temporal representations with Fourier-embedded conditioning on physical parameters, the model functions as a fast surrogate capable of accurately predicting complex field evolution on previously unseen cases. To our knowledge, this represents one of the first demonstrations of a bidirectional…
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Taxonomy
TopicsAdvanced Fiber Laser Technologies · Optical Network Technologies · Photonic Crystal and Fiber Optics
