Physics-Informed Neural Networks and Extensions
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866929479162527744 |
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| author | Raissi, Maziar Perdikaris, Paris Ahmadi, Nazanin Karniadakis, George Em |
| author_facet | Raissi, Maziar Perdikaris, Paris Ahmadi, Nazanin Karniadakis, George Em |
| contents | In this paper, we review the new method Physics-Informed Neural Networks (PINNs) that has become the main pillar in scientific machine learning, we present recent practical extensions, and provide a specific example in data-driven discovery of governing differential equations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16806 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Physics-Informed Neural Networks and Extensions Raissi, Maziar Perdikaris, Paris Ahmadi, Nazanin Karniadakis, George Em Machine Learning Artificial Intelligence In this paper, we review the new method Physics-Informed Neural Networks (PINNs) that has become the main pillar in scientific machine learning, we present recent practical extensions, and provide a specific example in data-driven discovery of governing differential equations. |
| title | Physics-Informed Neural Networks and Extensions |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2408.16806 |