Improve the Fitting Accuracy of Deep Learning for the Nonlinear Schrödinger Equation Using Linear Feature Decoupling Method

This work addresses a domain-specific challenge in computational physics by incrementally enhancing model performance for solving nonlinear partial differential equations.

The paper tackled the problem of improving deep learning's fitting accuracy for the Nonlinear Schrödinger Equation by using the Feature Decoupling Distributed method, resulting in a significant reduction in NLSE loss compared to non-decoupling models.