Physics‐Informed Neural Network for Nonlinear Dynamics in Fiber Optics

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 optics. A systematic investigation and comprehensive verification on PINN for multiple physical effects in optical fibers is carried out, including dispersion, self‐phase modulation, and higher‐order nonlinear effects. Moreover, both the special case (soliton propagation) and general case (multipulse propagation) are investigated and realized with PINN. In existing studies, PINN is mainly effective for a single scenario. To overcome this problem, the physical parameters (i.e., pulse peak power and amplitudes of subpulses) are hereby embedded as additional input parameter controllers, which allow PINN to learn the physical constraints of different scenarios and perform good generalizability. Furthermore, PINN exhibits better performance than the data‐driven neural network using much less data, and its computational complexity (in terms of number of multiplications) is much lower than that of the split‐step Fourier method. The results show that PINN is not only an effective partial differential equation solver, but also a prospective technique to advance the scientific computing and automatic modeling in fiber optics. A physics‐informed neural network (PINN) is studied to solve the nonlinear Schrödinger equation for learning nonlinear dynamics in fiber optics. A systematic investigation of PINN for characterizing multiple physical effects in optical fibers is carried out. Physical parameters are embedded as controllers, making PINN perform good generalizability. Furthermore, the accuracy and complexity of PINN are also discussed and analyzed.