Data-Driven multiscale modeling in mechanics

We present a Data-Driven framework for multiscale mechanical analysis of materials. The proposed framework relies on the Data-Driven formulation in mechanics (Kirchdoerfer and Ortiz 2016), with the material data being directly extracted from lower-scale computations. Particular emphasis is placed on two key elements: the parametrization of material history, and the optimal sampling of the mechanical state space. We demonstrate an application of the framework in the prediction of the behavior of sand, a prototypical complex history-dependent material. In particular, the model is able to predict the material response under complex nonmonotonic loading paths, and compares well against plane strain and triaxial compression shear banding experiments.

Automated summary

The study introduces a Data-Driven framework for multiscale mechanical analysis of materials, which relies on material data extracted from lower-scale computations. It emphasizes on parametrization of material history and optimal sampling of mechanical state space. An application of the framework in sand behavior prediction shows good performance in predicting material response under complex loading paths.