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 010 (CoRR, 2023)

Asymptotic-Preserving Convolutional DeepONets Capture the Diffusive Behavior of the Multiscale Linear Transport Equations. - Keke Wu; Xiong-Bin Yan; Shi Jin 003; Zheng Ma 010 (CoRR, 2023)

An Unsupervised Deep Learning Approach for the Wave Equation Inverse Problem. - Xiong-Bin Yan; Keke Wu; Zhi-Qin John Xu; Zheng Ma 010 (CoRR, 2023)

Identifying a fractional order and a time-dependent coefficient in a time-fractional diffusion wave equation. - Xiong-Bin Yan; Ting Wei (J. Comput. Appl. Math., 2023)

ODE-DPS: ODE-based Diffusion Posterior Sampling for Inverse Problems in Partial Differential Equation. - Enze Jiang; Jishen Peng; Zheng Ma 010; Xiong-Bin Yan (CoRR, 2024)

Bayesian Inversion with Neural Operator (BINO) for modeling subdiffusion: Forward and inverse problems. - Xiong-Bin Yan; Zhi-Qin John Xu; Zheng Ma 010 (J. Comput. Appl. Math., 2025)

ODE-DPS: ODE-Based Diffusion Posterior Sampling for Linear Inverse Problems in Partial Differential Equation. - Enze Jiang; Jishen Peng; Zheng Ma 010; Xiong-Bin Yan (J. Sci. Comput., 2025)