Premium
A hybrid approach for the multi‐scale simulation of irreversible material behavior incorporating neural networks
Author(s) -
Hütter Geralf,
Settgast Christoph,
Lange Nils,
Abendroth Martin,
Kiefer Björn
Publication year - 2021
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.202000248
Subject(s) - artificial neural network , scale (ratio) , computer science , finite element method , yield (engineering) , code (set theory) , materials science , computational science , artificial intelligence , structural engineering , engineering , composite material , physics , programming language , set (abstract data type) , quantum mechanics
The present contribution presents a hybrid approach for the multi‐scale modeling where the yield surface and evolution equations are represented by neural networks, for which micro‐scale simulations are used as training data. The approach and its implementation into a commercial finite element code are demonstrated for a ductile foam material. The results are verified by comparison with an FE 2 simulation.