Abstract

Physics-informed neural networks (PINNs) have recently emerged as an important and ground-breaking technique in scientific machine learning for numerous applications including in optical fiber communications. However, the vanilla/baseline version of PINNs is prone to fail under certain conditions because of the nature of the physics-based regularization term in its loss function. The use of this unique regularization technique results in a highly complex non-convex loss landscape when visualized. This leads to failure modes in PINN-based modeling. The baseline PINN works very well as an optical fiber model with relatively simple fiber parameters and for uncomplicated transmission tasks. Yet, it struggles when the modeling task becomes relatively complex, reaching very high error, for example, numerous modeling tasks/scenarios in soliton communication and soliton pulse development in special fibers such as erbium-doped dispersion compensating fibers. We implement two methods to circumvent the limitations caused by the physics-based regularization term to solve this problem, namely, the so-called scaffolding technique for PINN modeling and the progressive block learning PINN modeling strategy to solve the nonlinear Schrödinger equation (NLSE), which models pulse propagation in an optical fiber. This helps PINN learn more accurately the dynamics of pulse evolution and increases accuracy by two to three orders of magnitude. We show in addition that this error is not due to the depth or architecture of the neural network but a fundamental issue inherent to PINN by design. The results achieved indicate a considerable reduction in PINN error for complex modeling problems, with accuracy increasing by up to two orders of magnitude.

© 2024 Optica Publishing Group

Virtual draw of microstructured optical fiber based on physics-informed neural networks

Jinmin Ding, Chenyang Hou, Yiming Zhao, Hongwei Liu, Zixia Hu, Fanchao Meng, and Sheng Liang
Opt. Express 32(6) 9316-9331 (2024)

Power evolution prediction of bidirectional Raman amplified WDM system based on PINN

Muyang Mei, Yuan Li, Mengchao Niu, Jiaye Zhu, Wei Li, Ming Luo, Zhongshuai Feng, Xuefeng Wu, Liang Mei, Qianggao Hu, Yi Jiang, and Xuefeng Yang
Opt. Express 32(4) 6587-6596 (2024)

Physics-informed neural networks for inverse problems in nano-optics and metamaterials

Yuyao Chen, Lu Lu, George Em Karniadakis, and Luca Dal Negro
Opt. Express 28(8) 11618-11633 (2020)

Modeling extended L-band fiber amplifiers using neural networks trained on experimental data

Hamed Rabbani, Saber Jalilpiran, Kaboko Jean-Jacques Monga, Sophie Larochelle, and Leslie A. Rusch
Opt. Express 32(9) 16182-16198 (2024)

Fast fiber nonlinearity compensation method for PDM coherent optical transmission systems based on the Fourier neural operator

Junling Huang, Anlin Yi, Lianshan Yan, Xingchen He, Lin Jiang, Hui Yang, Bin Luo, and Wei Pan
Opt. Express 32(2) 2245-2256 (2024)