10 citations · 10 across the 4 of their papers we have counts for
4 papers
A multi-scale feature enhanced graph neural network for fluid dynamics prediction in complex geometries
Li Xiao, Tianyu Li, Yiye Zou +2
Industrial design in fields such as vehicle and aerospace engineering often relies on large-scale numerical simulations to evaluate fluid dynamics performance, which can incur subs…
Optimize discrete loss with finite-difference physics constraint and time-stepping for PDE solving
Yali Luo, Yiye Zou, Heng Zhang +4
Computational Fluid Dynamics (CFD) is an important approach for analyzing flow phenomena and predicting engineering-relevant quantities. The governing physics is formulated as part…
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…
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…