May 14, 2024, 4:42 a.m. | Jesus Gonzalez-Sieiro, David Pardo, Vincenzo Nava, Victor M. Calo, Markus Towara

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.07441v1 Announce Type: new
Abstract: We propose a method for reducing the spatial discretization error of coarse computational fluid dynamics (CFD) problems by enhancing the quality of low-resolution simulations using a deep learning model fed with high-quality data. We substitute the default differencing scheme for the convection term by a feed-forward neural network that interpolates velocities from cell centers to face values to produce velocities that approximate the fine-mesh data well. The deep learning framework incorporates the open-source CFD code …

abstract arxiv cfd computational cs.lg data deep learning deep learning framework dynamics embedded error fed fluid dynamics framework low physics.flu-dyn quality quality data resolution simulations spatial type

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