@misc{holzapfel_predictive_constitutive_2021, 
author={Holzapfel, G.,Linka, K.,Sherifova, S.,Cyron, C.},
title={Predictive constitutive modelling of arteries by deep learning},
year={2021},
howpublished = {journal article},
doi = {https://doi.org/10.1098/rsif.2021.0411},
abstract = {The constitutive modelling of soft biological tissues has rapidly gained attention over the last 20 years. Current constitutive models can describe the mechanical properties of arterial tissue. Predicting these properties from microstructural information, however, remains an elusive goal. To address this challenge, we are introducing a novel hybrid modelling framework that combines advanced theoretical concepts with deep learning. It uses data from mechanical tests, histological analysis and images from second-harmonic generation. In this first proof of concept study, our hybrid modelling framework is trained with data from 27 tissue samples only. Even such a small amount of data is sufficient to be able to predict the stress–stretch curves of tissue samples with a median coefficient of determination of R2 = 0.97 from microstructural information, as long as one limits the scope to tissue samples whose mechanical properties remain in the range commonly encountered. This finding suggests that deep learning could have a transformative impact on the way we model the constitutive properties of soft biological tissues.},
note = {Online available at: \url{https://doi.org/10.1098/rsif.2021.0411} (DOI). Holzapfel, G.; Linka, K.; Sherifova, S.; Cyron, C.: Predictive constitutive modelling of arteries by deep learning. Journal of the Royal Society Interface. 2021. vol. 18, no. 182, 20210411. DOI: 10.1098/rsif.2021.0411}}