In solid mechanics, data-driven approaches are widely considered as the new paradigm that
can overcome the classic problems of constitutive models such as limiting hypothesis …

This paper introduces a novel physics-informed multi-agents constitutive model to propose
prediction in quasi-static constitutive behavior of cross-linked elastomer and the loss of …

Data-driven hyperelasticity shows great promise for modeling the mechanical response of
rubberlike materials. It enables an automated linkage between experimental data and …

The extraordinary success of Machine Learning (ML) in many complex heuristic fields has
promoted its introduction in more analytical engineering fields, improving or substituting …

This paper introduces three innovative frameworks to obtain the strain energy function for
rubber-like materials through an inverse approach. To this end, we inspire from the statistical …

We question the network affinity assumption in modeling chain orientations under polymer
deformations, and the use of the stretch measure projected from the right Cauchy–Green …

Based on our recent findings on the chains orientational distribution in deformed rubber-like
materials, we present a novel, accurate and simple non-affine model for isotropic …

Auxetic materials, which have negative Poisson's ratio, show potential to be used in many
interesting applications. Finite element analysis (FEA) is an important phase in …

Determining the mathematical structure of the stored energy of hyperelastic materials that
predicts the experimental data well can prove to be complicated. For instance, the …

This paper presents a new methodology to extend the Data-Driven Identification (DDI) to
heterogeneous samples made of multiple elastic materials. By using the Correspondence …