Automated constitutive modeling of isotropic hyperelasticity based on artificial neural networks
Abstract
Herein, an artificial neural network (ANN)-based approach for the efficient automated modeling and simulation of isotropic hyperelastic solids is presented. Starting from a large data set comprising deformations and corresponding stresses, a simple, physically based reduction of the problem’s dimensionality is performed in a data processing step. More specifically, three deformation type invariants serve as the input instead of the deformation...
Paper Details
Title
Automated constitutive modeling of isotropic hyperelasticity based on artificial neural networks
Published Date
Oct 6, 2021
Journal
Volume
69
Issue
1
Pages
213 - 232
Citation AnalysisPro
You’ll need to upgrade your plan to Pro
Looking to understand the true influence of a researcher’s work across journals & affiliations?
- Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
- Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.
Notes
History