Predicting the vector optical soliton propagation in birefringent fibers via EMPINN

The polarization effect significantly impacts the propagation of vector soliton in birefringent fibers, crucial for long-distance optical communication. With the development of artificial intelligence, deep learning methods, notably Physics-Informed Neural Network (PINN), have emerged as effective tools for solving Partial Differential Equations (PDE). In this work, we propose an Enhanced Multi-subnetwork PINN (EMPINN) with coupled double loss functions to predict vector soliton propagation. The advanced network accounts for predicting the propagation of vector soliton considered linear, elliptic, and circular polarization effects in two typical birefringent fibers. Across various cases, the predicted results exhibit low and reasonable errors compared to ground truth. The enhanced PINN method demonstrates stability, which reduces reliance on initial conditions, and improves computational efficiency. This approach offers a robust means to forecast ultrafast pulse propagation affected by polarization effects, advancing optical communication studies.