2 citations · 2 across the 4 of their papers we have counts for
Showing 2023Show all
2 papers · 1 filter
cs.LG2023
Capturing the Diffusive Behavior of the Multiscale Linear Transport Equations by Asymptotic-Preserving Convolutional DeepONets
Keke Wu, Xiong-bin Yan, Shi Jin +1
In this paper, we introduce two types of novel Asymptotic-Preserving Convolutional Deep Operator Networks (APCONs) designed to address the multiscale time-dependent linear transpor…
math.NA2023★ 2 cited
Laplace-fPINNs: Laplace-based fractional physics-informed neural networks for solving forward and inverse problems of subdiffusion
Xiong-Bin Yan, Zhi-Qin John Xu, Zheng Ma
The use of Physics-informed neural networks (PINNs) has shown promise in solving forward and inverse problems of fractional diffusion equations. However, due to the fact that autom…