.png){kind=icon}
返回
Dynamics-augmented cluster-based network model
.png){kind=icon}
OA
AI
DOI：10.1017/jfm.2024.428.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this study we propose a novel data-driven reduced-order model for complex dynamics, including nonlinear, multi-attractor, multi-frequency and multiscale behaviours. The starting point is a fully automatable cluster-based network model (CNM) (Li et al., J. Fluid Mech., vol. 906, 2021, A21) that kinematically coarse grains the state with clusters and dynamically predicts the transitions in a network model. In the proposed dynamics-augmented CNM (dCNM) the prediction error is reduced with trajectory-based clustering using the same number of centroids. The dCNM is first exemplified for the Lorenz system and then demonstrated for the three-dimensional sphere wake featuring periodic, quasi-periodic and chaotic flow regimes. For both plants, the dCNM significantly outperforms the CNM in resolving the multi-frequency and multiscale dynamics. This increased prediction accuracy is obtained by stratification of the state space aligned with the direction of the trajectories. Thus, the dCNM has numerous potential applications to a large spectrum of shear flows, even for complex dynamics.
Keyword:
low-dimensional models
wakes
期刊
IF:
3.9
论文数:
2.0W
被引数:
9.4W
机构
引用论文
A reduced-order model of three-dimensional unsteady flow in a cavity based on the resolvent operator
OAAI
OAAI分享收藏Cluster-based network modeling-From snapshots to complex dynamical systems.png)
SCIENCE ADVANCESOAAI
SCIENCE ADVANCESIF12.5
OAAI分享收藏Discovering governing equations from data by sparse identification of nonlinear dynamical systems通过非线性动力系统的稀疏识别从数据中发现控制方程OAAI
OAAI分享收藏分享收藏
查看更多内容