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We present a novel implementation of conditional Long Short-Term MemoryRecurrent Neural Networks that successfully predict the spectral evolution of apulse in nonlinear periodically-poled waveguides. The developed networks offerlarge flexibility by allowing the propagation of optical pulses with ranges ofenergies and temporal widths in waveguides with different poling periods. Theresults show very high agreement with the traditional numerical models.Moreover, we are able to use a single network to calculate both the real andimaginary parts of the pulse complex envelope, allowing for successfullyretrieving the pulse temporal and spectral evolution using the same network.

Conditional Recurrent Neural Networks for broad applications in nonlinear optics