Generalization Properties of Machine Learning-based Raman Models

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.