activity
20202025
most citedPINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs

21 citations · 26 across the 5 of their papers we have counts for

collaborators

5 papers

math.NA2025

Convergence analysis of a second-order SAV-ZEC scheme for the Cahn-Hilliard-Navier-Stokes system

Jingwei Sun, Zeyu Xia, Wei Zhang

Incorporating the scalar auxiliary variable (SAV) method and the zero energy contribution (ZEC) technique, we analyze a linear and fully decoupled numerical scheme for the Cahn-Hil…

math.NA2025

Optimal error estimates for 2D/3D incompressible Cahn--Hilliard--magnetohydrodynamic equations

Haiyan Su, Jilu Wang, Zeyu Xia +1

This paper focuses on an optimal error analysis of a fully discrete finite element scheme for the Cahn--Hilliard--magnetohydrodynamic (CH-MHD) system. The method use the standard i…

cs.LG2023★ 21 cited

PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs

Zhongkai Hao, Jiachen Yao, Chang Su +8

While significant progress has been made on Physics-Informed Neural Networks (PINNs), a comprehensive comparison of these methods across a wide range of Partial Differential Equati…

math.NA2022★ 1 cited

Optimal error estimates of a Crank--Nicolson finite element projection method for magnetohydrodynamic equations

Cheng Wang, Jilu Wang, Zeyu Xia +1

In this paper, we propose and analyze a fully discrete finite element projection method for the magnetohydrodynamic (MHD) equations. A modified Crank--Nicolson method and the Galer…

math.NA2020★ 4 cited

Optimal error estimates of a second-order projection finite element method for magnetohydrodynamic equations

Cheng Wang, Jilu Wang, Zeyu Xia +1

In this paper, we propose and analyze a temporally second-order accurate, fully discrete finite element method for the magnetohydrodynamic (MHD) equations. A modified Crank--Nicols…