A feed-forward neural network as a nonlinear dynamics integrator for supercontinuum generation

Abstract

The nonlinear propagation of ultrashort pulses in optical fiber depends sensitively on both input pulse and fiber parameters. As a result, optimizing propagation for specific applications generally requires time-consuming simulations based on sequential integration of the generalized nonlinear Schrödinger equation (GNLSE). Here, we train a feed-forward neural network to learn the differential propagation dynamics of the GNLSE, allowing emulation of direct numerical integration of fiber propagation, and particularly the highly complex case of supercontinuum generation. Comparison with a recurrent neural network shows that the feed-forward approach yields faster training and computation, and reduced memory requirements. The approach is generic and can be extended to other physical systems.

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Cited by in corpus (2)

• Low-complexity full-field ultrafast nonlinear dynamics prediction by a convolutional feature separation modeling method
• Conditional Recurrent Neural Networks for broad applications in nonlinear optics