most citedLaplace-fPINNs: Laplace-based fractional physics-informed neural networks for solving forward and inverse problems of subdiffusion

2 citations · 2 across the 2 of their papers we have counts for

collaborators

3 papers

math.NA2024

ODE-DPS: ODE-based Diffusion Posterior Sampling for Inverse Problems in Partial Differential Equation

Enze Jiang, Jishen Peng, Zheng Ma +1

In recent years we have witnessed a growth in mathematics for deep learning, which has been used to solve inverse problems of partial differential equations (PDEs). However, most d…

math.NA2023

An Unsupervised Deep Learning Approach for the Wave Equation Inverse Problem

Xiong-Bin Yan, Keke Wu, Zhi-Qin John Xu +1

Full-waveform inversion (FWI) is a powerful geophysical imaging technique that infers high-resolution subsurface physical parameters by solving a non-convex optimization problem. H…

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…