Recalibration Learning: Enabling Universal Transfer of ML Model of Gain and NF for Remote Optically Pumped Amplifiers

We demonstrate a novel, physical assumptions-based method – recalibration learning, that transfers Gain and Noise Figure ML models across remote optically pumped amplifiers. Spectral measurements over just two configurations on a target device ensure reliable transfer.