A Deep Learning Method for Elliptic Hemivariational Inequalities
From MaRDI portal
Publication:5074898
DOI10.4208/eajam.081121.161121zbMath1485.65070OpenAlexW4226132817MaRDI QIDQ5074898
Haoqin Wang, Chunmei Wang, Jian-Guo Huang
Publication date: 10 May 2022
Published in: Unnamed Author (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.4208/eajam.081121.161121
Artificial neural networks and deep learning (68T07) Computing methodologies and applications (68U99) Numerical methods for variational inequalities and related problems (65K15)
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations
- Weak adversarial networks for high-dimensional partial differential equations
- Nonconvex energy functions. Hemivariational inequalities and substationarity principles
- On the limited memory BFGS method for large scale optimization
- A bundle-Newton method for nonsmooth unconstrained minimization
- Multilayer feedforward networks are universal approximators
- The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems
- Finite element method for hemivariational inequalities. Theory, methods and applications
- Minimization principles for elliptic hemivariational inequalities
- DGM: a deep learning algorithm for solving partial differential equations
- A discontinuous Galerkin method for an elliptic hemivariational inequality for semipermeable media
- Solving high-dimensional eigenvalue problems using deep neural networks: a diffusion Monte Carlo like approach
- Int-Deep: a deep learning initialized iterative method for nonlinear problems
- A physics-guided neural network framework for elastic plates: comparison of governing equations-based and energy-based approaches
- A priori estimates of the population risk for two-layer neural networks
- Virtual element method for an elliptic hemivariational inequality with applications to contact mechanics
- An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Solving for high-dimensional committor functions using artificial neural networks
- Neural algorithm for solving differential equations
- A hemivariational inequality in crack problems
- Relu Deep Neural Networks and Linear Finite Elements
- Optimization and nonsmooth analysis
- Generalized Gradients and Applications
- Numerical treatment of problems involving nonmonotone boundary or stress-strain laws
- Universal approximation bounds for superpositions of a sigmoidal function
- Comparison of two methods for the solution of a class of nonconvex energy problems using convex minimization algorithms
- Double Bundle Method for finding Clarke Stationary Points in Nonsmooth DC Programming
- Optimization Methods for Large-Scale Machine Learning
- Survey of Bundle Methods for Nonsmooth Optimization
- Korn's inequalities for piecewise $H^1$ vector fields
- Solving high-dimensional partial differential equations using deep learning
- Adaptive Deep Learning for High-Dimensional Hamilton--Jacobi--Bellman Equations
- Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks
- Machine Learning and Computational Mathematics
- Deep Network Approximation Characterized by Number of Neurons
- On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs
- Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions
- Numerical analysis of hemivariational inequalities in contact mechanics
- Numerical Analysis of Elliptic Hemivariational Inequalities
This page was built for publication: A Deep Learning Method for Elliptic Hemivariational Inequalities
