Constructing coarse-grained models with physics-guided Gaussian process regression
Coarse-grained models describe the macroscopic mean response of a process at large scales, which derives from stochastic processes at small scales. Common examples include accounting for velocity fluctuations in a turbulent fluid flow model and cloud evolution in climate models. Most existing techni...
| Main Authors: | , , , |
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| Formato: | Artigo |
| Idioma: | English |
| Publicado em: |
AIP Publishing LLC
2024-06-01
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| Colecção: | APL Machine Learning |
| Acesso em linha: | http://dx.doi.org/10.1063/5.0190357 |