activity
20192025
most citedA comparison study of deep Galerkin method and deep Ritz method for elliptic problems with different boundary conditions

35 citations · 82 across the 11 of their papers we have counts for

collaborators

12 papers

math.NA2025

Numerical Homogenization of Landau-Lifshitz Equation with Rough Coefficients

Zetao Ma, Jingrun Chen, Rui Du +1

In this work, we develop a numerical homogenization approach for the fully nonlinear Landau-Lifshitz equation with rough coefficients, including non-periodicity and nonseparable sc…

math.NA2024

Error Analysis of the Deep Mixed Residual Method for High-order Elliptic Equations

Mengjia Bai, Jingrun Chen, Rui Du +1

This paper presents an a priori error analysis of the Deep Mixed Residual method (MIM) for solving high-order elliptic equations with non-homogeneous boundary conditions, including…

math.NA2023

Micromagnetics simulations and phase transitions of ferromagnetics with Dzyaloshinskii-Moriya interaction

Panchi Li, Shuting Gu, Jin Lan +3

Magnetic skyrmions widely exist in a diverse range of magnetic systems, including chiral magnets with a non-centrosymmetric structure characterized by Dzyaloshinkii-Moriya interact…

math.NA2021

A second-order semi-implicit method for the inertial Landau-Lifshitz-Gilbert equation

Panchi Li, Lei Yang, Jin Lan +2

Recent theoretical and experimental advances show that the inertia of magnetization emerges at sub-picoseconds and contributes to the ultrafast magnetization dynamics which cannot…

physics.comp-ph2021★ 1 cited

Advantages of a semi-implicit scheme over a fully implicit scheme for Landau-Lifshitz-Gilbert equation

Yifei Sun, Jingrun Chen, Rui Du +1

Magnetization dynamics in magnetic materials is modeled by the Landau-Lifshitz-Gilbert (LLG) equation. In the LLG equation, the length of magnetization is conserved and the system…

math.NA2021

Roughness Index for Loss Landscapes of Neural Network Models of Partial Differential Equations

Keke Wu, Xiangru Jian, Rui Du +2

Loss landscape is a useful tool to characterize and compare neural network models. The main challenge for analysis of loss landscape for the deep neural networks is that they are g…

math.NA2021★ 3 cited

A Gauss-Seidel projection method with the minimal number of updates for stray field in micromagnetic simulations

Panchi Li, Zetao Ma, Rui Du +1

Magnetization dynamics in magnetic materials is often modeled by the Landau-Lifshitz equation, which is solved numerically in general. In micromagnetic simulations, the computation…

math.NA2020★ 10 cited

Enforcing exact boundary and initial conditions in the deep mixed residual method

Liyao Lyu, Keke Wu, Rui Du +1

In theory, boundary and initial conditions are important for the wellposedness of partial differential equations (PDEs). Numerically, these conditions can be enforced exactly in cl…

math.NA2020★ 35 cited

A comparison study of deep Galerkin method and deep Ritz method for elliptic problems with different boundary conditions

Jingrun Chen, Rui Du, Keke Wu

Recent years have witnessed growing interests in solving partial differential equations by deep neural networks, especially in the high-dimensional case. Unlike classical numerical…

cs.CE2019★ 5 cited

Numerical methods for antiferromagnetics

Panchi Li, Jingrun Chen, Rui Du +1

Compared with ferromagnetic counterparts, antiferromagnetic materials are considered as the future of spintronic applications since these materials are robust against the magnetic…

math.NA2019★ 4 cited

Quasi-Monte Carlo sampling for machine-learning partial differential equations

Jingrun Chen, Rui Du, Panchi Li +1

Solving partial differential equations in high dimensions by deep neural network has brought significant attentions in recent years. In many scenarios, the loss function is defined…

math.NA2019★ 24 cited

Two improved Gauss-Seidel projection methods for Landau-Lifshitz-Gilbert equation

Panchi Li, Changjian Xie, Rui Du +2

In this paper, we present two improved Gauss-Seidel projection methods with unconditional stability. The first method updates the gyromagnetic term and the damping term simultaneou…