Dispersion Characterization and Pulse Prediction with Machine Learning
This work addresses experimental simplification and signal enhancement for optical communication and sensing methods, both classical and quantum, though it appears incremental as it applies existing neural network methods to a specific domain problem.
The paper tackles the problem of characterizing dispersive media and predicting pulse propagation through nonlinear dispersive media using neural networks, showing that trained networks can predict pulse profiles and dispersive features nearly identical to experimental counterparts while simplifying the setup by requiring only a single probe pulse instead of frequency scanning.
In this work we demonstrate the efficacy of neural networks in the characterization of dispersive media. We also develop a neural network to make predictions for input probe pulses which propagate through a nonlinear dispersive medium, which may be applied to predicting optimal pulse shapes for a desired output. The setup requires only a single pulse for the probe, providing considerable simplification of the current method of dispersion characterization that requires frequency scanning across the entirety of the gain and absorption features. We show that the trained networks are able to predict pulse profiles as well as dispersive features that are nearly identical to their experimental counterparts. We anticipate that the use of machine learning in conjunction with optical communication and sensing methods, both classical and quantum, can provide signal enhancement and experimental simplifications even in the face of highly complex, layered nonlinear light-matter interactions.