Parsimonious neural networks learn interpretable physical laws
Abstract Machine learning is playing an increasing role in the physical sciences and significant progress has been made towards embedding domain knowledge into models. Less explored is its use to discover interpretable physical laws from data. We propose parsimonious neural networks (PNNs) that comb...
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| Auteurs principaux: | , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Nature Portfolio
2021
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| Accès en ligne: | https://doaj.org/article/7b24e1e0a740421f8df33a3f6cfc3068 |
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