Principle Driven Parameterized Fiber Model based on GPT-PINN Neural Network

• Main Authors: Zang, Yubin, Hua, Boyu, Tang, Zhenzhou, Lin, Zhipeng, Zhang, Fangzheng, Li, Simin, Zhang, Zuxing, Chen, Hongwei
• Format: Journal Article
• Language: English
• Published: 19-08-2024
• Subjects: Computer Science - Artificial Intelligence
• DOI: 10.48550/arxiv.2408.09951

In cater the need of Beyond 5G communications, large numbers of data driven
artificial intelligence based fiber models has been put forward as to utilize
artificial intelligence's regression ability to predict pulse evolution in
fiber transmission at a much faster speed compared with the traditional split
step Fourier method. In order to increase the physical interpretabiliy,
principle driven fiber models have been proposed which inserts the Nonlinear
Schodinger Equation into their loss functions. However, regardless of either
principle driven or data driven models, they need to be re-trained the whole
model under different transmission conditions. Unfortunately, this situation
can be unavoidable when conducting the fiber communication optimization work.
If the scale of different transmission conditions is large, then the whole
model needs to be retrained large numbers of time with relatively large scale
of parameters which may consume higher time costs. Computing efficiency will be
dragged down as well. In order to address this problem, we propose the
principle driven parameterized fiber model in this manuscript. This model
breaks down the predicted NLSE solution with respect to one set of transmission
condition into the linear combination of several eigen solutions which were
outputted by each pre-trained principle driven fiber model via the reduced
basis method. Therefore, the model can greatly alleviate the heavy burden of
re-training since only the linear combination coefficients need to be found
when changing the transmission condition. Not only strong physical
interpretability can the model posses, but also higher computing efficiency can
be obtained. Under the demonstration, the model's computational complexity is
0.0113% of split step Fourier method and 1% of the previously proposed
principle driven fiber model.