FastLRNR and Sparse Physics Informed Backpropagation
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911375452798976 |
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| author | Cho, Woojin Lee, Kookjin Park, Noseong Rim, Donsub Welper, Gerrit |
| author_facet | Cho, Woojin Lee, Kookjin Park, Noseong Rim, Donsub Welper, Gerrit |
| contents | We introduce Sparse Physics Informed Backpropagation (SPInProp), a new class of methods for accelerating backpropagation for a specialized neural network architecture called Low Rank Neural Representation (LRNR). The approach exploits the low rank structure within LRNR and constructs a reduced neural network approximation that is much smaller in size. We call the smaller network FastLRNR. We show that backpropagation of FastLRNR can be substituted for that of LRNR, enabling a significant reduction in complexity. We apply SPInProp to a physics informed neural networks framework and demonstrate how the solution of parametrized partial differential equations is accelerated. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_04001 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FastLRNR and Sparse Physics Informed Backpropagation Cho, Woojin Lee, Kookjin Park, Noseong Rim, Donsub Welper, Gerrit Machine Learning Artificial Intelligence Numerical Analysis 68T07, 65D25, 65M22 We introduce Sparse Physics Informed Backpropagation (SPInProp), a new class of methods for accelerating backpropagation for a specialized neural network architecture called Low Rank Neural Representation (LRNR). The approach exploits the low rank structure within LRNR and constructs a reduced neural network approximation that is much smaller in size. We call the smaller network FastLRNR. We show that backpropagation of FastLRNR can be substituted for that of LRNR, enabling a significant reduction in complexity. We apply SPInProp to a physics informed neural networks framework and demonstrate how the solution of parametrized partial differential equations is accelerated. |
| title | FastLRNR and Sparse Physics Informed Backpropagation |
| topic | Machine Learning Artificial Intelligence Numerical Analysis 68T07, 65D25, 65M22 |
| url | https://arxiv.org/abs/2410.04001 |