Zhiping Mao

According to our database, Zhiping Mao authored at least 20 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Efficient and stable SAV-based methods for gradient flows arising from deep learning.

Ziqi Ma, Zhiping Mao, Jie Shen

J. Comput. Phys., 2024

2023

DeepStSNet: Reconstructing the quantum state-resolved thermochemical nonequilibrium flowfield using deep neural operator learning with scarce data.

Jiaqi Lv, Qizhen Hong, Xiaoyong Wang, Zhiping Mao, Quanhua Sun

J. Comput. Phys., October, 2023

Analysis and Hermite Spectral Approximation of Diffusive-Viscous Wave Equations in Unbounded Domains Arising in Geophysics.

Dan Ling, Zhiping Mao

J. Sci. Comput., May, 2023

Operator Learning Enhanced Physics-informed Neural Networks for Solving Partial Differential Equations Characterized by Sharp Solutions.

Bin Lin, Zhiping Mao, Zhicheng Wang, George Em Karniadakis

CoRR, 2023

Physics-informed neural networks with residual/gradient-based adaptive sampling methods for solving PDEs with sharp solutions.

Zhiping Mao, Xuhui Meng

CoRR, 2023

2022

A spectral method for stochastic fractional PDEs using dynamically-orthogonal/bi-orthogonal decomposition.

Yue Zhao, Zhiping Mao, Ling Guo, Yifa Tang, George Em Karniadakis

J. Comput. Phys., 2022

Learning functional priors and posteriors from data and physics.

Xuhui Meng, Liu Yang, Zhiping Mao, José del Águila Ferrandis, George Em Karniadakis

J. Comput. Phys., 2022

Physics-informed neural networks for inverse problems in supersonic flows.

Ameya D. Jagtap, Zhiping Mao, Nikolaus A. Adams, George Em Karniadakis

J. Comput. Phys., 2022

2021

DeepXDE: A Deep Learning Library for Solving Differential Equations.

Lu Lu, Xuhui Meng, Zhiping Mao, George Em Karniadakis

SIAM Rev., 2021

DeepM&Mnet for hypersonics: Predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators.

Zhiping Mao, Lu Lu, Olaf Marxen, Tamer A. Zaki, George Em Karniadakis

J. Comput. Phys., 2021

Physics-informed neural networks (PINNs) for fluid mechanics: A review.

Shengze Cai, Zhiping Mao, Zhicheng Wang, Minglang Yin, George Em Karniadakis

CoRR, 2021

2020

What is the fractional Laplacian? A comparative review with new results.

Anna Lischke, Guofei Pang, Mamikon A. Gulian, Fangying Song, Christian Glusa, Xiaoning Zheng, Zhiping Mao, Wei Cai, Mark M. Meerschaert, Mark Ainsworth, George Em Karniadakis

J. Comput. Phys., 2020

2019

A Spectral Penalty Method for Two-Sided Fractional Differential Equations with General Boundary Conditions.

Nan Wang, Zhiping Mao, Chengming Huang, George E. Karniadakis

SIAM J. Sci. Comput., 2019

2018

A Spectral Method (of Exponential Convergence) for Singular Solutions of the Diffusion Equation with General Two-Sided Fractional Derivative.

Zhiping Mao, George E. Karniadakis

SIAM J. Numer. Anal., 2018

Spectral element method with geometric mesh for two-sided fractional differential equations.

Zhiping Mao, Jie Shen

Adv. Comput. Math., 2018

2017

A Generalized Spectral Collocation Method with Tunable Accuracy for Fractional Differential Equations with End-Point Singularities.

Fanhai Zeng, Zhiping Mao, George E. Karniadakis

SIAM J. Sci. Comput., 2017

Hermite Spectral Methods for Fractional PDEs in Unbounded Domains.

Zhiping Mao, Jie Shen

SIAM J. Sci. Comput., 2017

Analysis and Approximation of a Fractional Cahn-Hilliard Equation.

Mark Ainsworth, Zhiping Mao

SIAM J. Numer. Anal., 2017

Fractional Burgers equation with nonlinear non-locality: Spectral vanishing viscosity and local discontinuous Galerkin methods.

Zhiping Mao, George E. Karniadakis

J. Comput. Phys., 2017

2016

Efficient spectral-Galerkin methods for fractional partial differential equations with variable coefficients.

Zhiping Mao, Jie Shen

J. Comput. Phys., 2016