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

DOI10.1002/nme.6869zbMath1526.74055OpenAlexW3208574486MaRDI QIDQ6061746

Mauricio Fernández, Felix Fritzen, Oliver Weeger

Publication date: 31 October 2023

Published in: International Journal for Numerical Methods in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/nme.6869

zbMATH Keywords

topology optimizationmachine learningartificial neural networkeffective constitutive equationmicrostructural instabilityelastic beam lattice metamaterial

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05) Rods (beams, columns, shafts, arches, rings, etc.) (74K10) Nonlinear elasticity (74B20) Effective constitutive equations in solid mechanics (74Q15) Thin bodies, structures (74K99) Topological methods for optimization problems in solid mechanics (74P15) Numerical and other methods in solid mechanics (74S99)

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