@inproceedings{2a8703b906f74e29837247e2949171f0,
title = "Solving the Nonlinear Schr{\"o}dinger Equation in Optical Fibers Using Physics-informed Neural Network",
abstract = "We constructed a physics-informed neural network (PINN) to solve the nonlinear Schr{\"o}dinger equation for different input waveforms. Results show that PINN can accurately characterize pulse evolution in fibers with less complexity to SSFM methods.",
author = "Xiaotian Jiang and Danshi Wang and Qirui Fan and Min Zhang and Chao Lu and {Tao Lau}, {Alan Pak}",
note = "Funding Information: This work was supported in part by National Natural Science Foundation of China (No. 61975020, 61871415), in part by Fund of State Key Laboratory of IPOC (BUPT) (No. IPOC2020ZT05), P. R. China. Publisher Copyright: {\textcopyright} 2021 OSA.; 2021 Optical Fiber Communications Conference and Exhibition, OFC 2021 ; Conference date: 06-06-2021 Through 11-06-2021",
year = "2021",
month = jun,
language = "English",
series = "2021 Optical Fiber Communications Conference and Exhibition, OFC 2021 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--3",
booktitle = "2021 Optical Fiber Communications Conference and Exhibition, OFC 2021 - Proceedings",
}