8 citations · 14 across the 5 of their papers we have counts for
5 papers
Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach
Qian Hu, Bin Fan, Yao Xiao +2
Physics-informed neural networks (PINNs) often face ill-posed optimization, competing losses, and parameter compensation in partial differential equation (PDE) inverse problems. Tr…
Synergizing Kolmogorov-Arnold Networks with Dynamic Adaptive Weighting for High-Frequency and Multi-Scale PDE Solutions
Guokan Chen, Yao Xiao, Bin Fan +3
PINNs enhance scientific computing by incorporating physical laws into neural network structures, leading to significant advancements in scientific computing. However, PINNs strugg…
Total variation regularization for recovering the spatial source term in a time-fractional diffusion equation
Bin Fan
In this paper, we consider an inverse space-dependent source problem for a time-fractional diffusion equation. To deal with the ill-posedness of the problem, we transform the probl…
Efficient numerical method for multi-term time-fractional diffusion equations with Caputo-Fabrizio derivatives
Bin Fan
In this paper, we consider a numerical method for the multi-term Caputo-Fabrizio time-fractional diffusion equations (with orders , ). The proposed met…
Identifying source term in the subdiffusion equation with L^2-TV regularization
Bin Fan, Chuanju Xu
In this paper, we consider the inverse source problem for the time-fractional diffusion equation, which has been known to be an ill-posed problem. To deal with the ill-posedness of…