Neural Networks as Material Models within a Multiscale Approach

2009 ◽  
Author(s):  
J.F. Unger ◽  
C. Könke
Keyword(s):  
Neural Networks ◽  
Multiscale Approach ◽  
Material Models

Neural networks as material models within a multiscale approach

2009 ◽  
Vol 87 (19-20) ◽  
pp. 1177-1186 ◽  
Author(s):  
Jörg F. Unger ◽  
Carsten Könke
Keyword(s):  
Neural Networks ◽  
Multiscale Approach ◽  
Material Models

scholarly journals Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks

2019 ◽  
Vol 162 ◽  
pp. 322-332 ◽  
Author(s):  
Max Schwarzer ◽  
Bryce Rogan ◽  
Yadong Ruan ◽  
Zhengming Song ◽  
Diana Y. Lee ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Brittle Material ◽  
Material Models ◽  
Fracture Evolution

scholarly journals Nonlinear multiscale simulation of elastic beam lattices with anisotropic homogenized constitutive models based on artificial neural networks

2021 ◽  
Author(s):  
Til Gärtner ◽  
Mauricio Fernández ◽  
Oliver Weeger
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Lattice Structure ◽  
Constitutive Models ◽  
Multiscale Simulation ◽  
Multiscale Approach ◽  
Perturbation Approach ◽  
Material Symmetry ◽  
Highly Nonlinear ◽  
Artificial Neural

AbstractA sequential nonlinear multiscale method for the simulation of elastic metamaterials subject to large deformations and instabilities is proposed. For the finite strain homogenization of cubic beam lattice unit cells, a stochastic perturbation approach is applied to induce buckling. Then, three variants of anisotropic effective constitutive models built upon artificial neural networks are trained on the homogenization data and investigated: one is hyperelastic and fulfills the material symmetry conditions by construction, while the other two are hyperelastic and elastic, respectively, and approximate the material symmetry through data augmentation based on strain energy densities and stresses. Finally, macroscopic nonlinear finite element simulations are conducted and compared to fully resolved simulations of a lattice structure. The good agreement between both approaches in tension and compression scenarios shows that the sequential multiscale approach based on anisotropic constitutive models can accurately reproduce the highly nonlinear behavior of buckling-driven 3D metamaterials at lesser computational effort.

Determination of S-N curves with the application of artificial neural networks

1999 ◽  
Vol 22 (8) ◽  
pp. 723-728 ◽  
Author(s):  
Artymiak ◽  
Bukowski ◽  
Feliks ◽  
Narberhaus ◽  
Zenner
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural

Review of Pattern Recognition by Self-Organizing Neural Networks.

1995 ◽  
Vol 40 (11) ◽  
pp. 1110-1110
Author(s):  
Stephen James Thomas
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Self Organizing
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