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Generalization Properties of Machine Learning-Based Raman Models (Th1A.28)

Presenter: Uiara de Moura, Danmarks Tekniske Universitet

We investigate the generalization capabilities of neural network-based Raman amplifier models. The new proposed model architecture, including fiber parameters as inputs, can predict Raman gains of fiber types unseen during training, unlike previous fiber-specific models.

Authors:Uiara de Moura, Danmarks Tekniske Universitet / Darko Zibar, Danmarks Tekniske Universitet / Margareth Rosa Brusin, Polito / Andrea Carena, Polito / Francesco Da Ros, Danmarks Tekniske Universitet


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