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

cs.LG updates on

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

Doctoral Researcher (m/f/div) in Automated Processing of Bioimages

@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena

Seeking Developers and Engineers for AI T-Shirt Generator Project

@ Chevon Hicks | Remote

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

Data Quality Intern

@ Syngenta Group | Toronto, Ontario, Canada