Convolutional autoencoder for the spatiotemporal latent representation of turbulence
This addresses the problem of nonlinear reduced-order modeling for turbulent flows, particularly those with extreme events, for researchers in fluid dynamics and machine learning, though it is incremental as it builds on existing autoencoder methods.
The paper tackled the challenge of obtaining an efficient reduced-order latent representation for turbulent flows with extreme events, using a 3D multiscale convolutional autoencoder that required less than 10% degrees of freedom compared to proper orthogonal decomposition and accurately reconstructed extreme flow states.
Turbulence is characterised by chaotic dynamics and a high-dimensional state space, which make this phenomenon challenging to predict. However, turbulent flows are often characterised by coherent spatiotemporal structures, such as vortices or large-scale modes, which can help obtain a latent description of turbulent flows. However, current approaches are often limited by either the need to use some form of thresholding on quantities defining the isosurfaces to which the flow structures are associated or the linearity of traditional modal flow decomposition approaches, such as those based on proper orthogonal decomposition. This problem is exacerbated in flows that exhibit extreme events, which are rare and sudden changes in a turbulent state. The goal of this paper is to obtain an efficient and accurate reduced-order latent representation of a turbulent flow that exhibits extreme events. Specifically, we employ a three-dimensional multiscale convolutional autoencoder (CAE) to obtain such latent representation. We apply it to a three-dimensional turbulent flow. We show that the Multiscale CAE is efficient, requiring less than 10% degrees of freedom than proper orthogonal decomposition for compressing the data and is able to accurately reconstruct flow states related to extreme events. The proposed deep learning architecture opens opportunities for nonlinear reduced-order modeling of turbulent flows from data.