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

TL;DR

This paper introduces a linear feature decoupling method to improve deep learning's ability to accurately fit the nonlinear Schrödinger equation, achieving significantly lower loss than traditional models.

Contribution

The paper proposes a novel feature decoupling approach that enhances deep learning performance for solving the nonlinear Schrödinger equation.

Findings

Reduced NLSE loss with the decoupling method

Enhanced fitting accuracy of deep learning models

Demonstrated effectiveness over non-decoupling models

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.