Controlling spatiotemporal nonlinearities in multimode fibers with deep neural networks

Abstract

Spatiotemporal nonlinear interactions in multimode fibers are of interest for beam shaping and frequency conversion by exploiting the nonlinear propagation of different pump regimes from quasi-continuous wave to ultrashort pulses centered around visible to infrared pump wavelengths. The nonlinear effects in multi-mode fibers depend strongly on the excitation condition, however relatively little work has been reported on this subject. Here, we present the first machine learning approach to learn and control the nonlinear frequency conversion inside multimode fibers by tailoring the excitation condition via deep neural networks. Trained with experimental data, deep neural networks are adapted to learn the relation between the spatial beam profile of the pump pulse and the spectrum generation. For different user-defined target spectra, network-suggested beam shapes are applied and control over the cascaded Raman scattering and supercontinuum generation processes are achieved. Our results present a novel method to tune the spectra of a broadband source.

arXiv comment12 pages, 10 figures

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Cited by in corpus (8)

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• Observing distant objects with a multimode fibre-based holographic endoscope
• Theory of Transverse Mode Instability in Fiber Amplifiers with Multimode Excitations
• Spectral Shaping in a Multimode Fiber by All-fiber Modulation
• Predicting mode-locked fiber laser output using feed-forward neural network
• Tutorial: How to build and control an all-fiber wavefront modulator using mechanical perturbations
• Machine learning-driven complex models for wavefront shaping through multimode fibers