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

arXiv: https://arxiv.org/abs/2411.04511 · HTML: https://mlsanity.com/paper/2411.04511/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 7 Nov 2024.

## Builds on (most cited first, 2 of 2)

- [Physics-Informed Neural Operator for Fast and Scalable Optical Fiber Channel Modelling in Multi-Span Transmission](https://mlsanity.com/paper/2208.08868/) (2022)
- [Fast and accurate waveform modeling of long-haul multi-channel optical fiber transmission using a hybrid model-data driven scheme](https://mlsanity.com/paper/2201.05502/) (2022)

## Abstract (arXiv)

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.