Hybrid approach to predict the effective properties of heterogeneous materials using artificial neural networks and micromechanical models
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Publication:6089273
DOI10.1002/nme.6877MaRDI QIDQ6089273
Unnamed Author, Yves Chemisky, Etienne Pruliere
Publication date: 17 November 2023
Published in: International Journal for Numerical Methods in Engineering (Search for Journal in Brave)
Artificial neural networks and deep learning (68T07) Inhomogeneity in solid mechanics (74E05) Micromechanics of solids (74M25) Effective constitutive equations in solid mechanics (74Q15) Homogenization in equilibrium problems of solid mechanics (74Q05) Numerical and other methods in solid mechanics (74S99)
Cites Work
- Unnamed Item
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- Simplified approach to the derivation of the relationship between Hill polarization tensors of transformed problems and applications
- Multilayer feedforward networks are universal approximators
- Cross-validation methods
- Micromechanics-based surrogate models for the response of composites: a critical comparison between a classical mesoscale constitutive model, hyper-reduction and neural networks
- A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths
- Solutions of inhomogeneity problems with graded shells and application to core-shell nanoparticles and composites
- Elastic properties of reinforced solids: Some theoretical principles
- The determination of the elastic field of an ellipsoidal inclusion, and related problems
- The elastic field outside an ellipsoidal inclusion
- Analysis of Composite Materials—A Survey
- On Computing the Points and Weights for Gauss--Legendre Quadrature