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Data-Driven Model Reduction for Stochastic Burgers Equations
We present a class of efficient parametric closure models for 1D stochastic Burgers equations. Casting it as statistical learning of the flow map, we derive the parametric form by representing the unresolved high wavenumber Fourier modes as functionals of the resolved variable’s trajectory. The redu...
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| Publicado no: | Entropy (Basel) |
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| Autor principal: | |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
MDPI
2020
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7760244/ https://ncbi.nlm.nih.gov/pubmed/33266339 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/e22121360 |
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