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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2501.09987 |
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| _version_ | 1866910788045766656 |
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| author | Xu, Zhi-Qin John Zhang, Lulu Cai, Wei |
| author_facet | Xu, Zhi-Qin John Zhang, Lulu Cai, Wei |
| contents | In this review, we survey the latest approaches and techniques developed to overcome the spectral bias towards low frequency of deep neural network learning methods in learning multiple-frequency solutions of partial differential equations. Open problems and future research directions are also discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_09987 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On understanding and overcoming spectral biases of deep neural network learning methods for solving PDEs Xu, Zhi-Qin John Zhang, Lulu Cai, Wei Numerical Analysis In this review, we survey the latest approaches and techniques developed to overcome the spectral bias towards low frequency of deep neural network learning methods in learning multiple-frequency solutions of partial differential equations. Open problems and future research directions are also discussed. |
| title | On understanding and overcoming spectral biases of deep neural network learning methods for solving PDEs |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2501.09987 |