Improve the Fitting Accuracy of Deep Learning for the Nonlinear Schrödinger Equation Using Linear Feature Decoupling Method
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929580905857024 |
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| author | Zhang, Yunfan Niu, Zekun Shi, Minghui Hu, Weisheng Yi, Lilin |
| author_facet | Zhang, Yunfan Niu, Zekun Shi, Minghui Hu, Weisheng Yi, Lilin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_04511 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improve the Fitting Accuracy of Deep Learning for the Nonlinear Schrödinger Equation Using Linear Feature Decoupling Method Zhang, Yunfan Niu, Zekun Shi, Minghui Hu, Weisheng Yi, Lilin Signal Processing Machine Learning 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. |
| title | Improve the Fitting Accuracy of Deep Learning for the Nonlinear Schrödinger Equation Using Linear Feature Decoupling Method |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2411.04511 |