68 citations · 108 across the 6 of their papers we have counts for
4 papers · 1 filter
An augmented Lagrangian deep learning method for variational problems with essential boundary conditions
Jianguo Huang, Haoqin Wang, Tao Zhou
This paper is concerned with a novel deep learning method for variational problems with essential boundary conditions. To this end, we first reformulate the original problem into a…
Adaptive Learning on the Grids for Elliptic Hemivariational Inequalities
Jianguo Huang, Chunmei Wang, Haoqin Wang
This paper introduces a deep learning method for solving an elliptic hemivariational inequality (HVI). In this method, an expectation minimization problem is first formulated based…
Friedrichs Learning: Weak Solutions of Partial Differential Equations via Deep Learning
Fan Chen, Jianguo Huang, Chunmei Wang +1
This paper proposes Friedrichs learning as a novel deep learning methodology that can learn the weak solutions of PDEs via a minmax formulation, which transforms the PDE problem in…
Int-Deep: A Deep Learning Initialized Iterative Method for Nonlinear Problems
Jianguo Huang, Haoqin Wang, Haizhao Yang
This paper focuses on proposing a deep learning initialized iterative method (Int-Deep) for low-dimensional nonlinear partial differential equations (PDEs). The corresponding frame…