2 papers
cs.LG2024
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…
cs.LG2024
Unsupervised Learning Method for the Wave Equation Based on Finite Difference Residual Constraints Loss
Xin Feng, Yi Jiang, Jia-Xian Qin +2
The wave equation is an important physical partial differential equation, and in recent years, deep learning has shown promise in accelerating or replacing traditional numerical me…