Automatic generation of interpretable hyperelastic material models by symbolic regression

Abdusalamov, Rasul; Hillgärtner, Markus; Itskov, Mikhail

doi:10.48550/arxiv.2206.15099

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…25 We also mention interpretable data-driven models based on an optimization procedure of generalized Mooney-Rivlin model 26 or on symbolic regressions. 27 The outline of the paper is as follows. In Section 2 we describe a numerical framework of modeling membrane deformation based on data-driven constitutive equations, whereas explicit formulas for the Laplace stretch case are presented in Appendix A.…”

Section: Introductionmentioning

confidence: 99%

Interpretable data‐driven modeling of hyperelastic membranes

Salamatova

Liogky

2023

Numer Methods Biomed Eng

View full text Add to dashboard Cite

We proposed an approach for interpretable data‐driven modeling of an isotropic incompressible hyperelastic membrane deformation. The approach is based on the response functions in terms of the Laplace stretch and the finite element method, where response functions are partial derivatives of a hyperelastic potential with respect to the chosen strain measure. The Laplace stretch as the strain measure allows us to recover directly the response functions from experimental data and construct automatically data‐driven constitutive relations. All needed formulas were obtained explicitly. We tested our approach for membrane inflation with data‐driven constitutive relations based on the perforated membrane extension tests.

show abstract

Section: Introductionmentioning

confidence: 99%