no code implementations • 21 Apr 2024 • Enze Jiang, Jishen Peng, Zheng Ma, Xiong-bin Yan
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).
no code implementations • 8 Nov 2023 • Xiong-bin Yan, Keke Wu, Zhi-Qin John Xu, Zheng Ma
Full-waveform inversion (FWI) is a powerful geophysical imaging technique that infers high-resolution subsurface physical parameters by solving a non-convex optimization problem.
no code implementations • 28 Jun 2023 • Keke Wu, Xiong-bin Yan, Shi Jin, Zheng Ma
In this paper, we introduce two types of novel Asymptotic-Preserving Convolutional Deep Operator Networks (APCONs) designed to address the multiscale time-dependent linear transport problem.
no code implementations • 3 Apr 2023 • Xiong-bin Yan, Zhi-Qin John Xu, Zheng Ma
To address this issue, this paper proposes an extension to PINNs called Laplace-based fractional physics-informed neural networks (Laplace-fPINNs), which can effectively solve the forward and inverse problems of fractional diffusion equations.
no code implementations • 22 Nov 2022 • Xiong-bin Yan, Zhi-Qin John Xu, Zheng Ma
A large number of numerical experiments demonstrate that the operator learning method proposed in this work can efficiently solve the forward problems and Bayesian inverse problems of the subdiffusion equation.