2021 **Abstract:** In this paper, we present two deep learning-based hybrid data-driven reduced-order models for prediction of unsteady fluid flows. These hybrid models rely on recurrent neural networks (RNNs) to evolve low-dimensional states of unsteady fluid flow. The first model projects the high-fidelity time series data from a finite element Navier–Stokes solver to a low-dimensional subspace via proper orthogonal decomposition (POD). The time-dependent coefficients in the POD subspace are propagated by the recurrent net (cl…

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“…2020; Xu & Duraisamy 2020; Bukka et al. 2021). Since a few studies have also recently recognized the challenges of the current form of CNN-AE for turbulence (Brunton, Hemati & Taira 2020; Fukami et al.…”

confidence: 99%

“…2020; Xu & Duraisamy 2020; Bukka et al. 2021). Since a few studies have also recently recognized the challenges of the current form of CNN-AE for turbulence (Brunton, Hemati & Taira 2020; Fukami et al.…”

confidence: 99%

“…Such hybrid architectures consider spatio-temporal domain knowledge and achieve data-driven time series predictions. Recently proposed POD-based DL-ROMs are the POD-CNN by Miyanawala and Jaiman 40 , the POD-RNN by Bukka et al 7 , the POD-enhanced autoencoders by Fresca and Manzoni 17 . These hybrid architectures have been demonstrated for 2D bluff body flows with and without fluid-structure interaction.…”

confidence: 99%

“…Many promising approaches have been established in the research community for a synergistic coupling of deep learning and physics-based models 7,9 . These models are trained to represent a full or partial parametrization of a forward process to reduce computational costs while emulating physical laws.…”

confidence: 99%

“…This latter defines a sparse representation through a linear combination of selected functions, and has been used for data-driven forecasting in fluid dynamics [49]. RNNs have been considered in [50,51] to evolve lowdimensional states of unsteady flows, exploiting either POD or a convolutional recurrent autoencoder to extract low-dimensional features from snapshots. DL algorithms have also been used in [52] to describe the reduced trial manifold where the approximation is sought, then relying on a minimum residual formulation to derive the ROM-hence, still requiring the assembling and the solution of a ROM as in traditional POD-Galerkin ROMs.…”

mentioning

confidence: 99%