Sampling and resolution characteristics in reduced order models of shallow water equations: Intrusive vs nonintrusive. (29th February 2020)

• Record Type: Journal Article
• Authors: Ahmed, Shady E.; San, Omer; Bistrian, Diana A.; Navon, Ionel M.
• Is Part Of: International journal for numerical methods in fluids. Volume 92:Number 8(2020)
• Page Start: 992
• Page End: 1036
• Publication Date: 2020-02-29
• Subjects: artificial neural network -- dynamic mode decomposition -- proper orthogonal decomposition -- reduced order modeling -- resolution -- sampling rate; Fluid dynamics -- Mathematics -- Periodicals; 532
• DOI: 10.1002/fld.4815
• Languages: English
• ISSNs: 0271-2091

Abstract

Summary: We investigate the sensitivity of reduced order models (ROMs) to training data spatial resolution as well as sampling rate. In particular, we consider proper orthogonal decomposition (POD), coupled with Galerkin projection (POD‐GP), as an intrusive ROM technique. For nonintrusive ROMs, we consider two frameworks. The first is using dynamic mode decomposition (DMD), and the second is based on artificial neural networks (ANNs). For ANN, we utilized a residual deep neural network, and for DMD we have studied two versions of DMD approaches; one with hard thresholding and the other with sorted bases selection. Also, we highlight the differences between mean subtracting the data (centering) and using the data without mean subtraction. We tested these ROMs using a system of 2D shallow water equations for four different numerical experiments, adopting combinations of sampling rates and spatial resolutions. For these cases, we found that the DMD basis obtained with hard thresholding is sensitive to sampling rate. The sorted DMD algorithm helps to mitigate this problem and yields more stabilized and converging solution. Furthermore, we demonstrate that both DMD approaches without mean subtraction provide significantly more accurate results than DMD with mean subtracting the data. On the other hand, POD is relatively insensitive to sampling rate and yields better representation of the flow field. Meanwhile, spatial resolution has little effect on both POD and DMD performances. Numerical results reveal that an ANN on POD subspace (POD‐ANN) performs remarkably better than POD‐GP and DMD in capturing system dynamics, even with a small number of modes. Abstract : In this study, we investigate the sensitivity of reduced order models (ROMs) to training data spatial resolution as well as sampling rate. In particular, we consider proper orthogonal decomposition (POD), coupled with Galerkin projection (POD‐GP), as an intrusive reduced order modeling technique. For non‐intrusive ROMs, we consider two frameworks. The first is using dynamic mode decomposition (DMD), and the second is based on artificial neural networks (ANNs). We tested these ROMs using a system of 2D shallow water equations for four different numerical experiments, adopting combinations of sampling rates and spatial resolutions. Overall, our results reveal that an ANN on POD subspace (POD‐ANN) performs remarkably better than POD‐GP and DMD in capturing system dynamics, even with a small number of modes.