Memory embedded non-intrusive reduced order modeling of non-ergodic flows

Abstract

Generating a digital twin of any complex system requires modeling and computational approaches that are efficient, accurate, and modular. Traditional reduced order modeling techniques are targeted at only the first two but the novel non-intrusive approach presented in this study is an attempt at taking all three into account effectively compared to their traditional counterparts. Based on dimensionality reduction using proper orthogonal decomposition (POD), we introduce a long short-term memory (LSTM) neural network architecture together with a principal interval decomposition (PID) framework as an enabler to account for localized modal deformation, which is a key element in accurate reduced order modeling of convective flows. Our applications for convection dominated systems governed by Burgers, Navier-Stokes, and Boussinesq equations demonstrate that the proposed approach yields significantly more accurate predictions than the POD-Galerkin method, and could be a key enabler towards near real-time predictions of unsteady flows.

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• Deep Learning for Time-Series Analysis
• DR-RNN: A deep residual recurrent neural network for model reduction
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Cited by in corpus (3)

• On closures for reduced order models $-$ A spectrum of first-principle to machine-learned avenues
• A data driven reduced order model of fluid flow by Auto-Encoder and self-attention deep learning methods
• Reduced order modeling of fluid flows: Machine learning, Kolmogorov barrier, closure modeling, and partitioning