Inputs Refinement with Incremental Learning for Accurate Digital Twin of Optical Networks

We propose a parameter refinement method based on incremental learning, leveraging multiple network snapshots to provide accurate estimated inputs (i.e., lumped losses, gain spectra, and offset noise) to digital twins, improving QoT prediction and optimization.