Thermodynamically consistent constitutive modeling of isotropic hyperelasticity based on artificial neural networks
Publikation: Beitrag in Fachzeitschrift › Konferenzartikel › Beigetragen
Beitragende
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
Originalsprache | Deutsch |
---|---|
Seiten (von - bis) | e202100144 |
Seitenumfang | 3 |
Fachzeitschrift | Proceedings in applied mathematics and mechanics : PAMM |
Jahrgang | 21 |
Ausgabenummer | 1 |
Publikationsstatus | Veröffentlicht - 2021 |
Peer-Review-Status | Nein |
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