We propose a new approach for data-driven automated discovery of isotropic hyperelastic constitutive laws. The is unsupervised, i.e., it requires no stress data but only displacement and global force data, which are realistically available through mechanical testing digital image correlation techniques; delivers interpretable models, models that embodied by parsimonious mathematical expressions...