Thermodynamically consistent constitutive modeling of isotropic hyperelasticity based on artificial neural networks

Publikation: Beitrag in FachzeitschriftKonferenzartikelBeigetragen

Abstract

Herein, a neural network-based constitutive model for isotropic hyperelastic solids which makes use of a physically motivated dimensionality reduction into the invariant space is presented. In order to automatically fulfill thermodynamic consistency, gradients of the network with respect to the input quantities are considered within a customized training loop. The proposed approach is exemplarily applied to the finite element simulation of two three-dimensional samples, while only data collected from pure two-dimensional virtual experiments are needed for the model calibration before.

Details

OriginalspracheDeutsch
Seiten (von - bis)e202100144
Seitenumfang3
FachzeitschriftProceedings in applied mathematics and mechanics : PAMM
Jahrgang21
Ausgabenummer1
PublikationsstatusVeröffentlicht - 2021
Peer-Review-StatusNein

Externe IDs

ORCID /0000-0003-2645-6770/work/142235675
ORCID /0000-0003-3358-1545/work/142237152
Mendeley 8eaa822b-2420-304f-8e7a-cc7d876226ad

Schlagworte

Schlagwörter

  • Thermodynamically consistent, Constitutive modeling, Artfificial neural networks, Hyperelasticity