activity
20192021
most citedA comparison study of deep Galerkin method and deep Ritz method for elliptic problems with different boundary conditions

35 citations · 50 across the 5 of their papers we have counts for

collaborators

7 papers

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-ph20211 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.NA202010 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.NA202035 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

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.NA20194 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…