What are the possibilities of machine learning techniques on the mechanical characterization of biological tissues?

computational simulation is the use of experimental data to estimate different material model parameters, resorting for that purpose to a strain energy function (SEF), all included in the continuum theory of large deformation hyper-elasticity. The constitutive modelling of soft biological tissues has recently constituted a very active field of research.8 These materials have commonly been modelled as hyperelastic continua embedded into continuum mechanical formulations. In this line, one of the main tasks considers the determination of appropriate strain energy density functions, from which local mechanical quantities are obtained. Several constitutive laws have been proposed for soft tissue modelling.1,9‒11, which may be suitable depending on the kind of soft biological tissue at stake. Holzapfel et al.12,13 proposed the most common SEFs for modelling the behavior of blood vessels accounting for two preferred directions, incorporating fiber dispersion with respect to the deterministic preferred orientation direction, and the work presented by Gasser et al.14 which includes microstructural information in the model by means of the assumption of a fiber orientation distribution function.