A representative volume element network (RVE-net) for accelerating RVE analysis, microscale material identification, and defect characterization
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Publication:2072746
DOI10.1016/j.cma.2021.114507OpenAlexW4206394974MaRDI QIDQ2072746
Publication date: 26 January 2022
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.2021.114507
artificial intelligencerepresentative volume elementparametric partial differential equationsdata-driven discoveryfully convolutional network
Micromechanics of solids (74M25) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65M99) Numerical and other methods in solid mechanics (74S99)
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Uses Software
Cites Work
- Routes for efficient computational homogenization of~nonlinear materials using the~proper generalized decompositions
- Multiple scale analysis of heterogeneous elastic structures using homogenization theory and Voronoi cell finite element method
- A model reduction method for the post-buckling analysis of cellular microstructures
- Alleviating mesh constraints: model reduction, parallel time integration and high resolution homogenization
- The reduced model multiscale method (R3M) for the nonlinear homogenization of hyperelastic media at finite strains
- Eigendeformation-based reduced order homogenization for failure analysis of heterogeneous materials
- Preprocessing and postprocessing for materials based on the homogenization method with adaptive finite element methods
- A comparison of homogenization and standard mechanics analyses for periodic porous composites
- Real functions for representation of rigid solids
- PhyGeoNet
- Two scale analysis of heterogeneous elastic-plastic materials with asymptotic homogenization and Voronoi cell finite element model
- A multilevel finite element method (FE\(^{2}\)) to describe the response of highly nonlinear structures using generalized continua.
- Nonuniform transformation field analysis
- \(FE^2\) multiscale approach for modelling the elastoviscoplastic behaviour of long fibre SiC/Ti composite materials
- Hidden physics models: machine learning of nonlinear partial differential equations
- Multilayer feedforward networks are universal approximators
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
- A numerical method for computing the overall response of nonlinear composites with complex microstructure
- A level set method for structural topology optimization.
- Effective properties of composite materials with periodic microstructure: A computational approach
- Prediction of the mechanical behavior of nonlinear heterogeneous systems by multi-level finite element modeling
- A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials
- 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
- Geometric deep learning for computational mechanics. I: Anisotropic hyperelasticity
- A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
- Exploring the 3D architectures of deep material network in data-driven multiscale mechanics
- Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
- Self-consistent clustering analysis: an efficient multi-scale scheme for inelastic heterogeneous materials
- A framework for data-driven analysis of materials under uncertainty: countering the curse of dimensionality
- Coupling lattice structure topology optimization with design-dependent feature evolution for additive manufactured heat conduction design
- A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Reduced basis hybrid computational homogenization based on a mixed incremental formulation
- Shape optimization with topological changes and parametric control
- Transformation field analysis of inelastic composite materials
- Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
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