Direct Prediction of Steady-State Flow Fields in Meshed Domain with Graph Networks

6 May 2021

Papers citing "Direct Prediction of Steady-State Flow Fields in Meshed Domain with Graph Networks"

10 / 10 papers shown

Title
C(NN)FD -- Deep Learning Modelling of Multi-Stage Axial Compressors Aerodynamics G. Bruni S. Maleki S. Krishnababu AI4CE 47 0 0 18 Mar 2025
Large-scale flood modeling and forecasting with FloodCast Qingsong Xu Yilei Shi Jonathan Bamber Chaojun Ouyang Xiao Xiang Zhu AI4CE 39 12 0 18 Mar 2024
Deep learning modelling of manufacturing and build variations on multi-stage axial compressors aerodynamics G. Bruni Md Tahmid Rahman Laskar Jimmy X. Huang AI4CE 18 0 0 06 Oct 2023
C(NN)FD -- a deep learning framework for turbomachinery CFD analysis G. Bruni S. Maleki S. Krishnababu AI4CE 11 4 0 09 Jun 2023
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variability F. Casenave B. Staber Xavier Roynard AI4CE 19 13 0 22 May 2023
A composable machine-learning approach for steady-state simulations on high-resolution grids Rishikesh Ranade C. Hill Lalit Ghule Jay Pathak AI4CE 17 7 0 11 Oct 2022
Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNN Yadi Cao Menglei Chai Minchen Li Chenfanfu Jiang AI4CE 21 18 0 05 Oct 2022
Deep Surrogate for Direct Time Fluid Dynamics Lucas Meyer Louen Pottier Alejandro Ribés Bruno Raffin AI4CE 11 7 0 16 Dec 2021
Spline-PINN: Approaching PDEs without Data using Fast, Physics-Informed Hermite-Spline CNNs Nils Wandel Michael Weinmann Michael Neidlin Reinhard Klein AI4CE 50 58 0 15 Sep 2021
SPNets: Differentiable Fluid Dynamics for Deep Neural Networks Connor Schenck D. Fox PINN 3DPC AI4CE 165 161 0 15 Jun 2018