68 citations · 108 across the 6 of their papers we have counts for
6 papers
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…
A primal-dual fixed point algorithm for multi-block convex minimization
Peijun Chen, Jianguo Huang, Xiaoqun Zhang
We extend a primal-dual fixed point algorithm (PDFP) proposed in [5] to solve two kinds of separable multi-block minimization problems, arising in signal processing and imaging sci…
A primal-dual fixed-point algorithm for minimization of the sum of three convex separable functions
Peijun Chen, Jianguo Huang, Xiaoqun Zhang
Many problems arising in image processing and signal recovery with multi-regularization can be formulated as minimization of a sum of three convex separable functions. Typically, t…