Physics-Informed Neural Operator for Fast and Scalable Optical Fiber Channel Modelling in Multi-Span Transmission postprint

Author: Yuchen Song Danshi Wang Qirui Fan Xiaotian Jiang Xiao Luo Min Zhang
Submit Time:2023-02-19 19:58:59

Abstract: 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.

Subject: Optics >> Quantum optics
Cite as: ChinaXiv:202303.00980 (or this version ChinaXiv:202303.00980V1)
DOI:10.12074/202303.00980V1
CSTR:32003.36.ChinaXiv.202303.00980.V1
TXID： 3138b6ea-4d65-4450-b396-2b83705d565d
Recommended references： Yuchen Song,Danshi Wang,Qirui Fan,Xiaotian Jiang,Xiao Luo,Min Zhang.Physics-Informed Neural Operator for Fast and Scalable Optical Fiber Channel Modelling in Multi-Span Transmission.中国科学院科技论文预发布平台.[ChinaXiv:202303.00980V1] (Click&Copy)

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ChinaXiv:202303.00980V1

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Physics-Informed Neural Operator for Fast and Scalable Optical Fiber Channel Modelling in Multi-Span Transmission postprint

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