.png){kind=icon}
.png){kind=icon}
Return
Convolution-weighting method for the physics-informed neural network: A-dual
.png){kind=icon}
PRE
AI
C
M
DOI：10.1016/j.jcp.2026.114773.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Physics-informed neural networks (PINNs) are extensively employed to solve partial differential equations (PDEs) by ensuring that the outputs and gradients of deep learning models adhere to the governing equations. However, constrained by computational limitations, PINNs are typically optimized using a finite set of points, which poses significant challenges in guaranteeing their convergence and accuracy. In this study, we proposed a new weighting scheme that will adaptively change the weights to the loss functions from isolated points to their continuous neighborhood regions. The empirical results show that our weighting scheme can reduce the relative L2 errors to a lower value.
Keywords:
Physics-informed neural network
Resampling
Convolution-weighting
Journal
IF:
3.8
Papers:
1.5W
Citations:
7.4W
Organization
Cited Papers