85 citations · 184 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 85 cited
A practical PINN framework for multi-scale problems with multi-magnitude loss terms
Yong Wang, Yanzhong Yao, Jiawei Guo +1
For multi-scale problems, the conventional physics-informed neural networks (PINNs) face some challenges in obtaining available predictions. In this paper, based on PINNs, we propo…
math.NA2023★ 33 cited
A deep learning method for multi-material diffusion problems based on physics-informed neural networks
Yanzhong Yao, Jiawei Guo, Tongxiang Gu
Given the facts of the extensiveness of multi-material diffusion problems and the inability of the standard PINN(Physics-Informed Neural Networks) method for such problems, in this…
math.NA2022★ 66 cited
Pre-training strategy for solving evolution equations based on physics-informed neural networks
Jiawei Guo, Yanzhong Yao, Han Wang +1
The physics informed neural network (PINN) is a promising method for solving time-evolution partial differential equations (PDEs). However, the standard PINN method may fail to sol…