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An artificial neural network framework for reduced order modeling of transient flows
[article]
2019
arXiv
pre-print
This paper proposes a supervised machine learning framework for the non-intrusive model order reduction of unsteady fluid flows to provide accurate predictions of non-stationary state variables when the control parameter values vary. Our approach utilizes a training process from full-order scale direct numerical simulation data projected on proper orthogonal decomposition (POD) modes to achieve an artificial neural network (ANN) model with reduced memory requirements. This data-driven ANN
arXiv:1802.09474v2
fatcat:x6hvga4mwnfmlf3c4qtuucppvi