Abstract In this paper we introduce constitutive artificial neural networks (CANNs), a novel machine learning architecture for data-driven modeling of the mechanical behavior materials. CANNs are able to incorporate by their very design information from three different sources, namely stress-strain data, theoretical knowledge materials theory, and diverse additional (e.g., about microstructure ...

In this research, the exponential stretched based hyperelastic strain energy was modified to provide the unstressed initial configuration. To this end, as the first step, the model was calibrated by the experimental data to find the best material parameters. The fitting results indicated material stability in large deformations and basic loading modes. In the second step, the initial pseudo str...

This paper presents the micromechanical modeling of fiber bridging constitutive law of a discontinuous fiber reinforced composite (DFRC) under cyclic loading. Fiist, the derivation of fiber bridging constitutive law under monotonic loading is briefly reviewed to highlight he approach to obtain the bridging stress carried by randomly distributed discontinuous fibers. Second, the relation between...

Computer-aided medical technologies such as simulators for surgical training and planning require accurate representation of soft tissue behavior under large deformations. Limited datasets, and unrealistic models for soft tissues currently hinder the advancement of surgical simulation. This work identifies the nonlinear mechanical response of liver through the development of a physically-based ...

The efficacy of continuum modeling in a variety granular intrusion scenarios is demonstrated. Besides providing new insights into the physics these cases, approach unifies many them under family basic constitutive relations.