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تسجيل مستخدم جديدMachine learning analysis of rogue solitons in supercontinuum generation
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Supercontinuum generation is a highly nonlinear process that exhibits unstable and chaotic characteristics when developing from long pump pulses injected into the anomalous dispersion regime of an optical fiber. A particular feature associated with this regime is the long-tailed rogue wave-like statistics of the spectral intensity on the long wavelength edge of the supercontinuum, linked to the generation of a small number of rogue solitons with extreme red-shifts. Here, we apply machine learning to analyze the characteristics of these solitons at the edge of the supercontinuum spectrum, and show how supervised learning can train a neural network to predict the peak power, duration, and temporal delay of these solitons from only the supercontinuum spectral intensity without phase information. The network accurately predicts soliton characteristics for a wide range of scenarios, from the onset of spectral broadening dominated by pure modulation instability to near octave-spanning supercontinuum with distinct rogue solitons.
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We present a numerical study of the evolution dynamics of ``optical rogue waves, statistically-rare extreme red-shifted soliton pulses arising from supercontinuum generation in photonic crystal fiber [D. R. Solli et al. Nature Vol. 450, 1054-1058 (20
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In the numerical modelling of cascaded mid-infrared (IR) supercontinuum generation (SCG) we have studied how an ensemble of spectrally and temporally distributed solitons from the long-wavelength part of an SC evolves and interacts when coupled into
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