Explicit and Implicit TVD Schemes for Conservation Laws with Caputo Derivatives. - Jian-Guo Liu 006; Zheng Ma 010; Zhennan Zhou (J. Sci. Comput., 2017)

The Discrete Stochastic Galerkin Method for Hyperbolic Equations with Non-smooth and Random Coefficients. - Shi Jin 003; Zheng Ma 010 (J. Sci. Comput., 2018)

An Upper Limit of Decaying Rate with Respect to Frequency in Linear Frequency Principle Model. - Tao Luo 012; Zheng Ma 010; Zhiwei Wang; Zhiqin John Xu; Yaoyu Zhang (MSML, 2022)

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 Neural Networks for Multiscale Kinetic Equations. - Shi Jin 003; Zheng Ma 010; Keke Wu (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)

Asymptotic-Preserving Neural Networks for Multiscale Time-Dependent Linear Transport Equations. - Shi Jin 003; Zheng Ma 010; Keke Wu (J. Sci. Comput., 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)