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

TL;DR

This paper introduces a principle-driven, parameterized fiber model based on GPT-PINN neural networks that predicts pulse evolution efficiently and with high physical interpretability, reducing re-training needs across different transmission conditions.

Contribution

The proposed model decomposes NLSE solutions into eigen solutions, enabling rapid adaptation to varying conditions with minimal re-training, improving efficiency and interpretability.

Findings

Computational complexity is only 0.0113% of the split step Fourier method.

Model achieves 1% of the complexity of previous principle-driven models.

Provides high physical interpretability and fast computation for fiber transmission prediction.

Abstract

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.