Generalized neural closure models with interpretability
Improving the predictive capability and computational cost of dynamical models is often at the heart of augmenting computational physics with machine learning (ML). However, most learning results are limited in interpretability and generalization over different computational grid resolutions, initia...
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| 格式: | 文件 |
| 语言: | English |
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Springer Science and Business Media LLC
2024
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| 在线阅读: | https://hdl.handle.net/1721.1/153818 |