Wavelength-division multiplexing (WDM) has historically enabled the increase in the
capacity of optical systems by progressively populating the existing optical bandwidth of …

A physics‐informed neural network (PINN) that combines deep learning with physics is
studied to solve the nonlinear Schrödinger equation for learning nonlinear dynamics in fiber …

Artificial Intelligence (AI) has been defined as a modern and intense scientific topic that
enables computer-based systems or embedded systems to divide and solve problems by …

The fiber channel model plays an essential role in the simulation and design of optical fiber
communication systems. However, it is difficult for conventional model-driven modeling to …

The modeling and prediction of the ultrafast nonlinear dynamics in the optical fiber are
essential for the studies of laser design, experimental optimization, and other fundamental …

We leverage the attention mechanism to investigate and comprehend the contribution of
each input symbol of the input sequence and their hidden representations for predicting the …

Transfer learning (TL) has been demonstrated its feasibility on fast remodeling for fiber
nonlinearity equalization. It will be very efficient with fine-tuning rather than retraining when …

Aircraft with a supersonic speed has a complex flow field around its optical window. The
precise modeling of the refractive index of the flow field is of great significance for the …

The impairments arising from the Kerr nonlinearity in optical fiber are a major obstacle in
fiber-optic transmission systems. To compensate for these impairments at the receiver, the …

Digital back-propagation (DBP) and learned DBP (LDBP) are proposed for nonlinearity
mitigation in WDM dual-polarization dispersion-managed systems. LDBP achieves Q-factor …