Deep learned finite elements
From MaRDI portal
Publication:2021024
DOI10.1016/J.CMA.2020.113401zbMath1506.65217OpenAlexW3084276559WikidataQ114196912 ScholiaQ114196912MaRDI QIDQ2021024
Phill-Seung Lee, Kyungho Yoon, Jae Ho Jung
Publication date: 26 April 2021
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cma.2020.113401
Artificial neural networks and deep learning (68T07) Finite element, Rayleigh-Ritz and Galerkin methods for boundary value problems involving PDEs (65N30)
Related Items (10)
Sparse Deep Neural Network for Nonlinear Partial Differential Equations ⋮ A sample-efficient deep learning method for multivariate uncertainty qualification of acoustic-vibration interaction problems ⋮ Learning finite element convergence with the multi-fidelity graph neural network ⋮ Data-driven synchronization-avoiding algorithms in the explicit distributed structural analysis of soft tissue ⋮ Locally assembled stiffness matrix: a novel method to obtain global stiffness matrix ⋮ Incompressible rubber thermoelasticity: a neural network approach ⋮ Learned Gaussian quadrature for enriched solid finite elements ⋮ A machine learning framework for accelerating the design process using CAE simulations: an application to finite element analysis in structural crashworthiness ⋮ Self-updated four-node finite element using deep learning ⋮ A new mesh smoothing method based on a neural network
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Finite element approximation of the Navier-Stokes equations
- The quadratic MITC plate and MITC shell elements in plate bending
- Nonlinear performance of continuum mechanics based beam elements focusing on large twisting behaviors
- Multilayer feedforward networks are universal approximators
- An arbitrary Lagrangian-Eulerian finite element method for transient dynamic fluid-structure interactions
- DGM: a deep learning algorithm for solving partial differential equations
- Computational mechanics enhanced by deep learning
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Deep Belief Networks Are Compact Universal Approximators
- A non-conforming element for stress analysis
- Higher‐order MITC general shell elements
- Numerical implementation of a neural network based material model in finite element analysis
- Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
- Learning representations by back-propagating errors
- Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
- A Fast Learning Algorithm for Deep Belief Nets
- A logical calculus of the ideas immanent in nervous activity
This page was built for publication: Deep learned finite elements