A deep neural network-based method for solving obstacle problems
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Publication:6158276
DOI10.1016/j.nonrwa.2023.103864MaRDI QIDQ6158276
Xiao-liang Cheng, Xing Shen, Kewei Liang, Xilu Wang
Publication date: 20 June 2023
Published in: Nonlinear Analysis. Real World Applications (Search for Journal in Brave)
Artificial intelligence (68Txx) Numerical methods for partial differential equations, boundary value problems (65Nxx) Numerical methods for mathematical programming, optimization and variational techniques (65Kxx)
Cites Work
- Unnamed Item
- Augmented Lagrangian active set methods for obstacle problems
- Numerical solution of the obstacle problem by the penalty method
- An algorithm for solving the double obstacle problems
- The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems
- Monotone multigrid methods for elliptic variational inequalities. II
- Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification
- ConvPDE-UQ: convolutional neural networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains
- Deep learning of dynamics and signal-noise decomposition with time-stepping constraints
- First-order least-squares method for the obstacle problem
- Galerkin least squares finite element method for the obstacle problem
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- An iterative algorithm based on the piecewise linear system for solving bilateral obstacle problems
- A Dynamical Method for Solving the Obstacle Problem
- SwitchNet: A Neural Network Model for Forward and Inverse Scattering Problems
- Approximation Theory of Multivariate Spline Functions in Sobolev Spaces
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