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The generation of an optical supercontinuum in nonlinear fibers exhibits highly complex nonlinear dynamics. Here, we show that one can train a neural network to learn the complex propagation dynamics for supercontinuum generation solely based on the input pulse parameters for a variety of scenarios ranging from higher-order soliton compression to broadband octave-spanning supercontinuum. The speed of our approach exceeds that of the direct integration of the generalized nonlinear Schrödinger equation by several orders of magnitude, allowing for “real-time” optimization or analysis of optical systems.
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Lauri Salmela, Mathilde Hary, John M. Dudley, Goëry Genty, "Neural network prediction of supercontinuum generation dynamics," Proc. SPIE PC12019, AI and Optical Data Sciences III, PC120190Y (9 March 2022); https://doi.org/10.1117/12.2608549