Exact Dirichlet boundary physics-informed neural network EPINN for solid mechanics
From MaRDI portal
Publication:6171233
DOI10.1016/j.cma.2023.116184MaRDI QIDQ6171233
No author found.
Publication date: 11 August 2023
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
finite elementtensor decompositionprinciple of least workexact Dirichlet boundary PINN (EPINN)meshless finite difference (MFD)Physics-informed Neural Network (PINN)
Cites Work
- Tensor Decompositions and Applications
- Multilayer feedforward networks are universal approximators
- The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems
- DGM: a deep learning algorithm for solving partial differential equations
- Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering
- A general deep learning framework for history-dependent response prediction based on UA-Seq2Seq model
- A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
- Hierarchical deep-learning neural networks: finite elements and beyond
- Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks
- HiDeNN-TD: reduced-order hierarchical deep learning neural networks
- A deep learning framework for constitutive modeling based on temporal convolutional network
- On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks
- Numerical Methods for Ordinary Differential Equations
This page was built for publication: Exact Dirichlet boundary physics-informed neural network EPINN for solid mechanics
