| Abstrakt: |
Data‐driven computational mechanics replaces phenomenological constitutive functions by performing numerical simulations based on data sets of representative samples in stress‐strain space. The distance of modeling values, e.g. stresses and strains in integration points of a finite element calculation, from the data set is minimized with respect to an appropriate metric, subject to equilibrium and compatibility constraints, see [1,2]. Although this method operates well for elasticity problems it challenges dealing with history‐dependent materials. One approach by [3], is the inclusion of local histories into the data set. However, it is still necessary to include models for the evolution of specific internal variables. In the presented approach, the data set is augmented with directions in the tangent space of points in stress‐strain space. Moreover, the data set is divided into subsets corresponding to different material behavior, e.g. elastic and inelastic. Based on the classification, transition rules map the modeling points to the various subsets. The convergence will be demonstrated by applying it to a model of elasto‐plasticity with isotropic hardening. [ABSTRACT FROM AUTHOR] |
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