May 8, 2024, 4:43 a.m. | Gabriele Immordino, Andrea Vaiuso, Andrea Da Ronch, Marcello Righi

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.04396v1 Announce Type: cross
Abstract: This paper focuses on addressing challenges posed by non-homogeneous unstructured grids, commonly used in Computational Fluid Dynamics (CFD). Their prevalence in CFD scenarios has motivated the exploration of innovative approaches for generating reduced-order models. The core of our approach centers on geometric deep learning, specifically the utilization of graph convolutional network (GCN). The novel Autoencoder GCN architecture enhances prediction accuracy by propagating information to distant nodes and emphasizing influential points. This architecture, with GCN layers …

abstract arxiv autoencoder cfd challenges computational convolutional core cs.ce cs.lg dynamics exploration fluid dynamics graph networks paper type unstructured

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

Senior DevOps Engineer- Autonomous Database

@ Oracle | Reston, VA, United States