Dynamics-augmented cluster-based network model

TL;DR

This paper introduces a dynamics-augmented cluster-based network model (dCNM) that improves prediction accuracy for complex nonlinear, multi-frequency, and multiscale dynamics in fluid systems by incorporating trajectory-based clustering.

Contribution

The paper presents a novel dCNM that enhances the existing CNM by reducing prediction errors through trajectory-based clustering, enabling better modeling of complex flow behaviors.

Findings

dCNM outperforms CNM in accuracy for Lorenz system

dCNM effectively captures multi-frequency dynamics in sphere wake

Trajectory-based clustering improves state space stratification

Abstract

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) which 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 implemented 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.