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Improve the Fitting Accuracy of Deep Learning for the Nonlinear Schr\"odinger Equation Using Linear Feature Decoupling Method

Signal Processing 2024-11-08 v1 Machine Learning
Authors: Yunfan Zhang , Zekun Niu , Minghui Shi , Weisheng Hu , Lilin Yi
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Abstract

We utilize the Feature Decoupling Distributed (FDD) method to enhance the capability of deep learning to fit the Nonlinear Schrodinger Equation (NLSE), significantly reducing the NLSE loss compared to non decoupling model.

Keywords

deep learning for image reconstruction fourier transform deep learning for detection and classification

Cite

@article{arxiv.2411.04511,
  title  = {Improve the Fitting Accuracy of Deep Learning for the Nonlinear Schr\"odinger Equation Using Linear Feature Decoupling Method},
  author = {Yunfan Zhang and Zekun Niu and Minghui Shi and Weisheng Hu and Lilin Yi},
  journal= {arXiv preprint arXiv:2411.04511},
  year   = {2024}
}

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