Physics-Informed Neural Operator for Fast and Scalable Optical Fiber Channel Modelling in Multi-Span Transmission
Not on the map. Why?
Abstract (arXiv)
We propose efficient modelling of optical fiber channel via NLSE-constrained physics-informed neural operator without reference solutions. This method can be easily scalable for distance, sequence length, launch power, and signal formats, and is implemented for ultra-fast simulations of 16-QAM signal transmission with ASE noise.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jul 2022.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2024: 2; 2025: 1.
3 papers.
- 2025Enabling Automatic Differentiation with Mollified Graph Neural Operatorscited by 6
- 2024Principle Driven Parameterized Fiber Model based on GPT-PINN Neural Networkcited by 0
- 2024Improve the Fitting Accuracy of Deep Learning for the Nonlinear Schr"odinger Equation Using Linear Feature Decoupling Methodcited by 0
- 2025Enabling Automatic Differentiation with Mollified Graph Neural Operators11 Apr 2025
- 2024Improve the Fitting Accuracy of Deep Learning for the Nonlinear Schr"odinger Equation Using Linear Feature Decoupling Method7 Nov 2024
- 2024Principle Driven Parameterized Fiber Model based on GPT-PINN Neural Network19 Aug 2024