In this contribution, we present a novel consistent dual-stage approach for
the automated generation of hyperelastic constitutive models which only
requires experimentally measurable data. To generate input data for our
approccio, an experiment with full-field measurement has to be conducted to
gather testing force and corresponding displacement field of the sample. Then,
in the first step of the dual-stage framework, a new finite strain Data-Driven
Identification (DDI) formulation is applied. This method enables to identify
tuples consisting of stresses and strains by only prescribing the applied
boundary conditions and the measured displacement field. In the second step,
the data set is used to calibrate a Physics-Augmented Neural Network (PANN),
which fulfills all common conditions of hyperelasticity by construction and is
very flexible at the same time. We demonstrate the applicability of our
approach by several descriptive examples. Two-dimensional synthetic data are
exemplarily generated in virtual experiments by using a reference constitutive
model. The calibrated PANN is then applied in 3D Finite Element simulations. In
addition, a real experiment including noisy data is mimicked.

Questo articolo esplora i giri e le loro implicazioni.

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