Convolution hierarchical deep-learning neural network (C-HiDeNN) with graphics processing unit (GPU) acceleration
From MaRDI portal
Publication:6164268
DOI10.1007/s00466-023-02329-4MaRDI QIDQ6164268
Tianju Xue, Daniel W. Apley, Chanwook Park, Gregory J. Wagner, Wing Kam Liu, Jiachen Guo, Sourav Saha, Satyajit Mojumder, Ye Lu
Publication date: 27 July 2023
Published in: Computational Mechanics (Search for Journal in Brave)
Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05) Finite element methods applied to problems in solid mechanics (74S05) Numerical and other methods in solid mechanics (74S99)
Related Items (3)
Deep learning discrete calculus (DLDC): a family of discrete numerical methods by universal approximation for STEM education to frontier research ⋮ Convolution hierarchical deep-learning neural networks (C-HiDeNN): finite elements, isogeometric analysis, tensor decomposition, and beyond ⋮ Convolution hierarchical deep-learning neural network tensor decomposition (C-HiDeNN-TD) for high-resolution topology optimization
Cites Work
- Advances in multiple scale kernel particle methods
- Reproducing kernel element method. I: Theoretical formulation
- Variational and projection methods for the volume constraint in finite deformation elasto-plasticity
- Meshless Galerkin method using radial basis functions based on domain decomposition
- Isogeometric analysis: CAD, finite elements, NURBS, exact geometry and mesh refinement
- Mixed finite element methods - reduced and selective integration techniques: a unification of concepts
- Generalized multiple scale reproducing kernel particle methods
- Analytical and computational assessment of locking in the \(hp\) finite element method
- Inferring solutions of differential equations using noisy multi-fidelity data
- Multilayer feedforward networks are universal approximators
- Compactly supported positive definite radial functions
- Piecewise polynomial, positive definite and compactly supported radial functions of minimal degree
- 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
- Hierarchical deep-learning neural networks: finite elements and beyond
- Multiresolution clustering analysis for efficient modeling of hierarchical material systems
- HiDeNN-TD: reduced-order hierarchical deep learning neural networks
- Self-consistent clustering analysis: an efficient multi-scale scheme for inelastic heterogeneous materials
- 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
- A meshfree unification: reproducing kernel peridynamics
- Hierarchical enrichment for bridging scales and mesh-free boundary conditions
- Meshless Galerkin methods using radial basis functions
- Wavelet and multiple scale reproducing kernel methods
- A point interpolation meshless method based on radial basis functions
- Reproducing kernel particle methods
- EAS‐elements for two‐dimensional, three‐dimensional, plate and shell structures and their equivalence to HR‐elements
- Theory and Applications of Smoothed Particle Hydrodynamics
- Deep learning discrete calculus (DLDC): a family of discrete numerical methods by universal approximation for STEM education to frontier research
- JAX-FEM: a differentiable GPU-accelerated 3D finite element solver for automatic inverse design and mechanistic data science
- Convolution hierarchical deep-learning neural networks (C-HiDeNN): finite elements, isogeometric analysis, tensor decomposition, and beyond
- Convolution hierarchical deep-learning neural network tensor decomposition (C-HiDeNN-TD) for high-resolution topology optimization
This page was built for publication: Convolution hierarchical deep-learning neural network (C-HiDeNN) with graphics processing unit (GPU) acceleration
