Isogeometric convolution hierarchical deep-learning neural network: isogeometric analysis with versatile adaptivity
DOI10.1016/j.cma.2023.116356OpenAlexW4386387954MaRDI QIDQ6147044
Satyajit Mojumder, Shao-Qiang Tang, Ye Lu, Yangfan Li, Sourav Saha, Wing Kam Liu, Hengyang Li, Jiachen Guo, Trevor Abbott, Gregory J. Wagner, Lei Zhang, Chanwook Park
Publication date: 15 January 2024
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.2023.116356
convolution hierarchical deep-learning neural network (C-hiDeNN)convolution isogeometric analysis (C-IGA)high-order smoothness and convergencer-h-p-s-a adaptive finite element method (FEM)software 2.0
Cites Work
- PGD-based \textit{computational vademecum} for efficient design, optimization and control
- Isogeometric analysis using T-splines
- Reproducing kernel element method. I: Theoretical formulation
- Isogeometric analysis: CAD, finite elements, NURBS, exact geometry and mesh refinement
- Smoothed particle hydrodynamics (SPH): an overview and recent developments
- Generalizing the finite element method: Diffuse approximation and diffuse elements
- Moving least-square reproducing kernel methods. I: Methodology and convergence
- Generalized multiple scale reproducing kernel particle methods
- Moving least-square reproducing kernel method. II: Fourier analysis
- Advanced separated spatial representations for hardly separable domains
- Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering
- Adaptive hyper reduction for additive manufacturing thermal fluid analysis
- Hierarchical deep-learning neural networks: finite elements and beyond
- HiDeNN-TD: reduced-order hierarchical deep learning neural networks
- Isogeometric analysis: an overview and computer implementation aspects
- Isogeometric analysis-suitable trivariate NURBS models from standard B-Rep models
- Towards an automatic isogeometric analysis suitable trivariate models generation -- application to geometric parametric analysis
- A meshfree unification: reproducing kernel peridynamics
- Analysis-suitable unstructured T-splines: multiple extraordinary points per face
- Hierarchical enrichment for bridging scales and mesh-free boundary conditions
- 3D NURBS-enhanced finite element method (NEFEM)
- Recent advances on the use of separated representations
- NURBS-enhanced finite element method (NEFEM)
- Swept Volume Parameterization for Isogeometric Analysis
- Meshless Galerkin methods using radial basis functions
- Element‐free Galerkin methods
- A point interpolation meshless method based on radial basis functions
- Reproducing kernel particle methods for structural dynamics
- Reproducing kernel particle methods
- Isogeometric contact: a review
- Isogeometric analysis of 3D solids in boundary representation for problems in nonlinear solid mechanics and structural dynamics
- JAX-FEM: a differentiable GPU-accelerated 3D finite element solver for automatic inverse design and mechanistic data science
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