1 citations · 1 across the 2 of their papers we have counts for
2 papers
math.NA2024
Combining physics-informed graph neural network and finite difference for solving forward and inverse spatiotemporal PDEs
Hao Zhang, Longxiang Jiang, Xinkun Chu +4
The great success of Physics-Informed Neural Networks (PINN) in solving partial differential equations (PDEs) has significantly advanced our simulation and understanding of complex…
cs.NE2022★ 1 cited
PhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural Network
Longxiang Jiang, Liyuan Wang, Xinkun Chu +2
Solving partial differential equations (PDEs) is an important research means in the fields of physics, biology, and chemistry. As an approximate alternative to numerical methods, P…