An introduction to kernel and operator learning methods for homogenization by self-consistent clustering analysis
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Publication:6159333
DOI10.1007/s00466-023-02331-warXiv2212.00802OpenAlexW4366086485MaRDI QIDQ6159333
Jiachen Guo, Wing Kam Liu, Sourav Saha, Owen Huang
Publication date: 1 June 2023
Published in: Computational Mechanics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2212.00802
Cites Work
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- A multiresolution continuum simulation of the ductile fracture process
- Proper general decomposition (PGD) for the resolution of Navier-Stokes equations
- The LATIN multiscale computational method and the proper generalized decomposition
- Existence et approximation des solutions des équations aux dérivées partielles et des équations de convolution
- Multiresolution modeling of ductile reinforced brittle composites
- The reduced model multiscale method (R3M) for the nonlinear homogenization of hyperelastic media at finite strains
- Linking microstructure and properties through a predictive multiresolution continuum
- Reproducing kernel particle methods for large deformation analysis of nonlinear structures
- Nonuniform transformation field analysis
- \(FE^2\) multiscale approach for modelling the elastoviscoplastic behaviour of long fibre SiC/Ti composite materials
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
- Adaptive hyper reduction for additive manufacturing thermal fluid analysis
- Concurrent \(n\)-scale modeling for non-orthogonal woven composite
- Adaptivity for clustering-based reduced-order modeling of localized history-dependent phenomena
- Self-consistent clustering analysis for multiscale modeling at finite strains
- Efficient multiscale modeling for woven composites based on self-consistent clustering analysis
- Self-consistent clustering analysis: an efficient multi-scale scheme for inelastic heterogeneous materials
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Clustering discretization methods for generation of material performance databases in machine learning and design optimization
- Prediction of aerodynamic flow fields using convolutional neural networks
- Multiresolution analysis for material design
- Two-scale FE-FFT- and phase-field-based computational modeling of bulk microstructural evolution and macroscopic material behavior
- A Hilbert Space Embedding for Distributions
- Introduction to Micromechanics and Nanomechanics
- Smoothed particle hydrodynamics: theory and application to non-spherical stars
- Transformation field analysis of inelastic composite materials
- Reproducing kernel particle methods
- Solving high-dimensional partial differential equations using deep learning
- Solution of Some Problems of Division: Part I. Division by a Polynomial of Derivation
- Three ways to solve partial differential equations with neural networks — A review
- Deep learning discrete calculus (DLDC): a family of discrete numerical methods by universal approximation for STEM education to frontier research
