LAUSR

The coupled nonlinear Schrödinger equation (CNLSE) governs signal propagation in polarization division multiplexed (PDM) optical fiber systems, yet poses significant numerical challenges. This paper introduces physics-informed neural networks (PINNs) as a novel framework for deterministic modeling of PDM transmission. Through validation across single-pulse evolution, communication sequences, and full PDM systems, PINNs demonstrate deterministic accuracy (RMSE=0.0044∼0.0129 and spectralerrors<4%) while overcoming traditional limitation. They eliminate the split-step Fourier method (SSFM)'s step-size dependencies and data-driven methods' statistical uncertainties. By preserving physical determinism through embedded PDE constraints, PINNs establish a new paradigm, to our knowledge, for reliable fiber-optic system modeling.