  We extend the scope of our approach for unsupervised automated discovery of
material laws (EUCLID) to the case of a material belonging to an unknown class
of behavior. To this end, we leverage the theory of generalized standard
materials, which encompasses a plethora of important constitutive classes. We
show that, based only on full-field kinematic measurements and net reaction
forces, EUCLID is able to automatically discover the two scalar thermodynamic
potentials, namely, the Helmholtz free energy and the dissipation potential,
which completely define the behavior of generalized standard materials. The a
priori enforced constraint of convexity on these potentials guarantees by
construction stability and thermodynamic consistency of the discovered model;
balance of linear momentum acts as a fundamental constraint to replace the
availability of stress-strain labeled pairs; sparsity promoting regularization
enables the automatic selection of a small subset from a possibly large number
of candidate model features and thus leads to a parsimonious, i.e., simple and
interpretable, model. Importantly, since model features go hand in hand with
the correspondingly active internal variables, sparse regression automatically
induces a parsimonious selection of the few internal variables needed for an
accurate but simple description of the material behavior. A fully automatic
procedure leads to the selection of the hyperparameter controlling the weight
of the sparsity promoting regularization term, in order to strike a
user-defined balance between model accuracy and simplicity. By testing the
method on synthetic data including artificial noise, we demonstrate that EUCLID
is able to automatically discover the true hidden material model from a large
catalog of constitutive classes, including elasticity, viscoelasticity,
elastoplasticity, viscoplasticity, isotropic and kinematic hardening.
