A monolithic hyper ROM \(\mathrm{FE}^2\) method with clustered training at finite deformations
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Publication:6118589
DOI10.1016/j.cma.2023.116522arXiv2306.02687MaRDI QIDQ6118589
Björn Kiefer, Nils Lange, Geralf Hütter
Publication date: 21 March 2024
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2306.02687
homogenizationmultiscalemonolithic scheme\(\mathrm{FE}^2\)reduced order modelling (ROM)hyper integration
Cites Work
- Unnamed Item
- Unnamed Item
- Study of efficient homogenization algorithms for nonlinear problems
- Multiscale methods for composites: A review
- Multi-scale computational homogenization: trends and challenges
- High-performance model reduction techniques in computational multiscale homogenization
- Two-scale computational homogenization of electro-elasticity at finite strains
- A numerical study of different projection-based model reduction techniques applied to computational homogenisation
- Computational micro-to-macro transitions of discretized microstructures undergoing small strains
- Isotropic constitutive models for metallic foams
- A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials
- Integration efficiency for model reduction in micro-mechanical analyses
- Smart constitutive laws: inelastic homogenization through machine learning
- A multiscale method for periodic structures using domain decomposition and ECM-hyperreduction
- High performance reduction technique for multiscale finite element modeling (HPR-FE\(^2\)): towards industrial multiscale FE software
- A multiscale high-cycle fatigue-damage model for the stiffness degradation of fiber-reinforced materials based on a mixed variational framework
- FFT-based homogenization of hypoelastic plasticity at finite strains
- Accelerating multiscale finite element simulations of history-dependent materials using a recurrent neural network
- Micromechanics-based surrogate models for the response of composites: a critical comparison between a classical mesoscale constitutive model, hyper-reduction and neural networks
- An efficient monolithic solution scheme for FE\(^2\) problems
- An FE-DMN method for the multiscale analysis of short fiber reinforced plastic components
- Dimensional hyper-reduction of nonlinear finite element models via empirical cubature
- Self-consistent clustering analysis: an efficient multi-scale scheme for inelastic heterogeneous materials
- The heterogeneous multiscale finite element method for the homogenization of linear elastic solids and a comparison with the FE\(^2\) method
- Model order reduction of nonlinear homogenization problems using a Hashin-Shtrikman type finite element method
- A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites
- Reduced basis hybrid computational homogenization based on a mixed incremental formulation
- Numerical identification of a viscoelastic substitute model for heterogeneous poroelastic media by a reduced order homogenization approach
- Computational homogenization with million-way parallelism using domain decomposition methods
- A coupled two-scale shell model with applications to layered structures
- Computational homogenization of nonlinear elastic materials using neural networks
- Finite rotation effects in numerical integration of rate constitutive equations arising in large-deformation analysis
- Least squares quantization in PCM
- A numerical two-scale homogenization scheme: the FE2-method
- Comparison of Some Reduced Representation Approximations
- On constitutive macro-variables for heterogeneous solids at finite strain
- Computation of non‐linear magneto‐electric product properties of 0–3 composites
- A comparative study on different neural network architectures to model inelasticity
- On the micromechanics of deep material networks
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