4 papers · 1 filter
Deep learning accelerated solutions of incompressible Navier-Stokes equations on non-uniform Cartesian grids
Heming Bai, Dong Zhang, Shengze Cai +1
In incompressible flow simulations, non-uniform grids efficiently capture localized flow features; however, their spatially varying resolutions severely exacerbate computational co…
Lagrangian-Eulerian learning of flow field and trajectories with TrajectoryFlowNet
Jingdi Wan, Hongping Wang, Bo Liu +5
Predicting particle transport in complex flows is traditionally achieved by solving the Navier-Stokes equations. While various numerical and experimental methods exist, they typica…
Transformer-based Neural Operators for 3D Wind Field Prediction over Complex Mountainous Terrain
Yujia Zhang, Jiaxi Qi, Ruiyan Chen +5
Accurate prediction of three-dimensional (3D) wind fields over complex mountainous terrain is essential for renewable energy deployment and regional weather modeling. Traditional c…
PiRD: Physics-informed Residual Diffusion for Flow Field Reconstruction
Siming Shan, Pengkai Wang, Song Chen +3
The use of machine learning in fluid dynamics is becoming more common to expedite the computation when solving forward and inverse problems of partial differential equations. Yet,…