4.4 Article

Dynamics-based machine learning of transitions in Couette flow

期刊

PHYSICAL REVIEW FLUIDS
卷 7, 期 8, 页码 -

出版社

AMER PHYSICAL SOC
DOI: 10.1103/PhysRevFluids.7.L082402

关键词

-

资金

  1. German National Science Foundation (DFG) [SPP 1881]

向作者/读者索取更多资源

This study derives low-dimensional models using data-driven methods to describe transitions among exact coherent states in plane Couette flow. These models can accurately predict off-SSM transitions that were not used in their training.
We derive low-dimensional, data-driven models for transitions among exact coherent states in one of the most studied canonical shear flows, the plane Couette flow. These one -or two-dimensional nonlinear models represent the leading-order reduced dynamics on attracting spectral submanifolds (SSMs), which we construct using the recently developed SSMLearn algorithm from a small number of simulated transitions. We find that the energy input and dissipation rates provide efficient parametrizations for the most important SSMs. By restricting the dynamics to these SSMs, we obtain reduced-order models that also reliably predict nearby, off-SSM transitions that were not used in their training.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.4
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据