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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.09084 |
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| _version_ | 1866913234408177664 |
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| author | Cho, Namkyeong Ryu, Junseung Hwang, Hyung Ju |
| author_facet | Cho, Namkyeong Ryu, Junseung Hwang, Hyung Ju |
| contents | This study investigates the impact of Sobolev Training on operator learning frameworks for improving model performance. Our research reveals that integrating derivative information into the loss function enhances the training process, and we propose a novel framework to approximate derivatives on irregular meshes in operator learning. Our findings are supported by both experimental evidence and theoretical analysis. This demonstrates the effectiveness of Sobolev Training in approximating the solution operators between infinite-dimensional spaces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09084 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sobolev Training for Operator Learning Cho, Namkyeong Ryu, Junseung Hwang, Hyung Ju Machine Learning Artificial Intelligence This study investigates the impact of Sobolev Training on operator learning frameworks for improving model performance. Our research reveals that integrating derivative information into the loss function enhances the training process, and we propose a novel framework to approximate derivatives on irregular meshes in operator learning. Our findings are supported by both experimental evidence and theoretical analysis. This demonstrates the effectiveness of Sobolev Training in approximating the solution operators between infinite-dimensional spaces. |
| title | Sobolev Training for Operator Learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2402.09084 |