Material modeling for parametric, anisotropic finite strain hyperelasticity based on machine learning with application in optimization of metamaterials | Zendy

Fernández Mauricio | Zendy; Fritzen Felix | Zendy; Weeger Oliver | Zendy

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Material modeling for parametric, anisotropic finite strain hyperelasticity based on machine learning with application in optimization of metamaterials

Author(s) -

Fernández Mauricio,

Fritzen Felix,

Weeger Oliver

Publication year - 2021

Publication title -

international journal for numerical methods in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.421

H-Index - 168

eISSN - 1097-0207

pISSN - 0029-5981

DOI - 10.1002/nme.6869

Subject(s) - hyperelastic material , metamaterial , isotropy , parametric statistics , constitutive equation , computer science , lattice (music) , anisotropy , parametric model , deformation (meteorology) , topology (electrical circuits) , nonlinear system , artificial neural network , topology optimization , finite element method , artificial intelligence , materials science , algorithm , structural engineering , mathematics , physics , engineering , optics , acoustics , composite material , statistics , combinatorics , quantum mechanics

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