Deep learning framework for multiscale finite element analysis based on data-driven mechanics and data augmentation (Q6171158)

scientific article; zbMATH DE number 7725678

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English	Deep learning framework for multiscale finite element analysis based on data-driven mechanics and data augmentation	scientific article; zbMATH DE number 7725678

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instance of

scholarly article

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title

Deep learning framework for multiscale finite element analysis based on data-driven mechanics and data augmentation (English)

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Computer Methods in Applied Mechanics and Engineering

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publication date

11 August 2023

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zbMATH Keywords

data-driven mechanics

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nonlinear homogenization

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multiscale finite element

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artificial neural network

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MaRDI profile type

MaRDI publication profile

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full work available at URL

https://doi.org/10.1016/j.cma.2023.116131

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cites work

A class of general algorithms for multi-scale analyses of heterogeneous media

0 references

Preprocessing and postprocessing for materials based on the homogenization method with adaptive finite element methods

0 references

Temporal homogenization of viscoelastic and viscoplastic solids subjected to locally periodic loading

0 references

Efficient micromechanical analysis of fiber-reinforced composites subjected to cyclic loading through time homogenization and reduced-order modeling

0 references

Fatigue life prediction using 2‐scale temporal asymptotic homogenization

0 references

A Dispersive Model for Wave Propagation in Periodic Heterogeneous Media Based on Homogenization With Multiple Spatial and Temporal Scales

0 references

\(FE^2\) multiscale approach for modelling the elastoviscoplastic behaviour of long fibre SiC/Ti composite materials

0 references

Multi-scale computational homogenization: trends and challenges

0 references

A multi-level computational model for multi-scale damage analysis in composite and porous materials

0 references

Computational homogenization of nonlinear elastic materials using neural networks

0 references

A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites

0 references

Data-driven multiscale finite element method: from concurrence to separation

0 references

Integration efficiency for model reduction in micro-mechanical analyses

0 references

High-performance model reduction techniques in computational multiscale homogenization

0 references

Dimensional hyper-reduction of nonlinear finite element models via empirical cubature

0 references

The reduced model multiscale method (R3M) for the nonlinear homogenization of hyperelastic media at finite strains

0 references

A model reduction method for the post-buckling analysis of cellular microstructures

0 references

A numerical method for computing the overall response of nonlinear composites with complex microstructure

0 references

Reduced order modeling strategies for computational multiscale fracture

0 references

\(\mathrm{FE}^2\) multiscale in linear elasticity based on parametrized microscale models using proper generalized decomposition

0 references

Eigendeformation-based reduced order homogenization for failure analysis of heterogeneous materials

0 references

A non-linear dimension reduction methodology for generating data-driven stochastic input models

0 references

Certification of projection-based reduced order modelling in computational homogenisation by the constitutive relation error

0 references

Self-consistent clustering analysis: an efficient multi-scale scheme for inelastic heterogeneous materials

0 references

Multiresolution clustering analysis for efficient modeling of hierarchical material systems

0 references

A mechanics‐informed artificial neural network approach in data‐driven constitutive modeling

0 references

Exploring the 3D architectures of deep material network in data-driven multiscale mechanics

0 references

A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials

0 references

A representative volume element network (RVE-net) for accelerating RVE analysis, microscale material identification, and defect characterization

0 references

Machine learning constitutive models of elastomeric foams

0 references

Data-driven computational mechanics

0 references

Data driven computing with noisy material data sets

0 references

Model-free data-driven inelasticity

0 references

Efficient data structures for model-free data-driven computational mechanics

0 references

Model-free data-driven computational mechanics enhanced by tensor voting

0 references

Data-driven fracture mechanics

0 references

Accelerating the distance-minimizing method for data-driven elasticity with adaptive hyperparameters

0 references

Variational framework for distance-minimizing method in data-driven computational mechanics

0 references

Finite element solver for data-driven finite strain elasticity

0 references

Data-based derivation of material response

0 references

An investigation on the coupling of data-driven computing and model-driven computing

0 references

Adaptive goal-oriented data sampling in data-driven computational mechanics

0 references

Neural network constitutive model for crystal structures

0 references

Identifiers

DOI

10.1016/j.cma.2023.116131

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Mathematics Subject Classification ID

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Sitelinks

Mathematics(1 entry)

mardi Publication:6171158