35 citations · 45 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 10 cited
Finite-difference-informed graph network for solving steady-state incompressible flows on block-structured grids
Yiye Zou, Tianyu Li, Lin Lu +4
Advances in deep learning have enabled physics-informed neural networks to solve partial differential equations. Numerical differentiation using the finite-difference (FD) method i…
physics.flu-dyn2024
A fully differentiable GNN-based PDE Solver: With Applications to Poisson and Navier-Stokes Equations
Tianyu Li, Yiye Zou, Shufan Zou +3
In this study, we present a novel computational framework that integrates the finite volume method with graph neural networks to address the challenges in Physics-Informed Neural N…
physics.flu-dyn2023★ 35 cited
Finite Volume Graph Network(FVGN): Predicting unsteady incompressible fluid dynamics with finite volume informed neural network
Tianyu Li, Shufan Zou, Xinghua Chang +2
The rapid development of deep learning has significant implications for the advancement of Computational Fluid Dynamics (CFD). Currently, most pixel-grid-based deep learning method…