Abstract

We perform a sparse identification of nonlinear dynamics (SINDy) for low-dimensionalized complex flow phenomena. We first apply the SINDy with two regression methods, the thresholded least square algorithm and the adaptive least absolute shrinkage and selection operator which show reasonable ability with a wide range of sparsity constant in our preliminary tests, to a two-dimensional single cylinder wake at$Re_D=100$, its transient process and a wake of two-parallel cylinders, as examples of high-dimensional fluid data. To handle these high-dimensional data with SINDy whose library matrix is suitable for low-dimensional variable combinations, a convolutional neural network-based autoencoder (CNN-AE) is utilized. The CNN-AE is employed to map a high-dimensional dynamics into a low-dimensional latent space. The SINDy then seeks a governing equation of the mapped low-dimensional latent vector. Temporal evolution of high-dimensional dynamics can be provided by combining the predicted latent vector by SINDy with the CNN decoder which can remap the low-dimensional latent vector to the original dimension. The SINDy can provide a stable solution as the governing equation of the latent dynamics and the CNN-SINDy-based modelling can reproduce high-dimensional flow fields successfully, although more terms are required to represent the transient flow and the two-parallel cylinder wake than the periodic shedding. A nine-equation turbulent shear flow model is finally considered to examine the applicability of SINDy to turbulence, although without using CNN-AE. The present results suggest that the proposed scheme with an appropriate parameter choice enables us to analyse high-dimensional nonlinear dynamics with interpretable low-dimensional manifolds.

Highlights

  • Sparse identification of nonlinear dynamics (SINDy) (Brunton, Proctor & Kutz 2016a) is one of the prominent data-driven tools to obtain governing equations of nonlinear dynamics in a form that we can understand

  • We present the results for high-dimensional flow examples with the convolutional neural network-based autoencoder (CNN-AE)/SINDy reduced-order modelling (ROM) in §§ 3.1, 3.2 and 3.3

  • We performed a SINDy for low-dimensionalized fluid flows and investigated influences of the regression methods and parameter considered for construction of SINDy-based modelling

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Summary

Introduction

Sparse identification of nonlinear dynamics (SINDy) (Brunton, Proctor & Kutz 2016a) is one of the prominent data-driven tools to obtain governing equations of nonlinear dynamics in a form that we can understand. The sparse regression idea has recently been propagated to turbulence closure modelling purposes (Beetham & Capecelatro 2020; Schmelzer, Dwight & Cinnella 2020; Beetham, Fox & Capecelatro 2021; Duraisamy 2021) As reported in these studies, by employing the SINDy to predict the temporal evolution of a system, we can obtain ordinary differential equations, which should be helpful to many applications, e.g. control of a system. In this way, the propagation of the use of SINDy can be seen in the fluid dynamics community

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