Transformer–Based Physics-Informed Neural Networks in Fiber Channel Modeling

This paper proposes a Transformer-PINN model integrating multi-head attention with physics-informed neural networks (PINN) to solve the nonlinear Schrödinger equation (NLSE), capturing long-term dependency in signal propagation and demonstrating generalization capabilities in distance and power.