Improve the Fitting Accuracy of Deep Learning for the Nonlinear Schrödinger Equation Using Linear Feature Decoupling Method
We utilize the Feature Decoupling Distributed (FDD) method to enhance the capability of deep learning to fit the Nonlinear Schrödinger Equation (NLSE), significantly reducing the NLSE loss compared to non decoupling model.
Authors
- Lilin Yi (ORCID: https://orcid.org/0000-0002-6039-9063)
- Weisheng Hu (ORCID: https://orcid.org/0000-0002-6168-2688)
- Zekun Niu
- Minghui Shi
- Yunfan Zhang (ORCID: https://orcid.org/0000-0003-3803-0996)
Institutions
- Shanghai Jiao Tong University (CN)
Publication Details
- Published
- 2024-11-02
- DOI
- https://doi.org/10.1109/acp/ipoc63121.2024.10809581
- Primary Topic
- Model Reduction and Neural Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China