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Found 5 papers, 2 papers with code
Flexible Raman Amplifier Optimization Based on Machine Learning-aided Physical Stimulated Raman Scattering Model
The forward propagation model is combined with an experimentally-trained ML model of a backward-pumping Raman amplifier to jointly optimize the frequency and power of the forward amplifier's pumps and the powers of the backward amplifier's pumps.
Comparison of Models for Training Optical Matrix Multipliers in Neuromorphic PICs
We experimentally compare simple physics-based vs. data-driven neural-network-based models for offline training of programmable photonic chips using Mach-Zehnder interferometer meshes.
Simultaneous gain profile design and noise figure prediction for Raman amplifiers using machine learning
A machine learning framework predicting pump powers and noise figure profile for a target distributed Raman amplifier gain profile is experimentally demonstrated.
Applied Physics Optics
Power Evolution Prediction and Optimization in a Multi-span System Based on Component-wise System Modeling
Cascades of a machine learning-based EDFA gain model trained on a single physical device and a fully differentiable stimulated Raman scattering fiber model are used to predict and optimize the power profile at the output of an experimental multi-span fully-loaded C-band optical communication system.
Machine learning-based EDFA Gain Model Generalizable to Multiple Physical Devices
We report a neural-network based erbium-doped fiber amplifier (EDFA) gain model built from experimental measurements.
