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We propose an automated computational algorithm for simultaneous modelselection and parameter identification for the hyperelastic mechanicalcharacterization of human brain tissue. Following the motive of the recentlyproposed computational framework EUCLID (Efficient Unsupervised ConstitutiveLaw Identitication and Discovery) and in contrast to conventional parametercalibration methods, we construct an extensive set of candidate hyperelasticmodels, i.e., a model library including popular models known from theliterature, and develop a computational strategy for automatically selecting amodel from the library that conforms to the available experimental data whilebeing represented as an interpretable symbolic mathematical expression. Thiscomputational strategy comprises sparse regression, i.e., a regression problemthat is regularized by a sparsity promoting penalty term that filters outirrelevant models from the model library, and a clustering method for groupingtogether highly correlated and thus redundant features in the model library.The model selection procedure is driven by labelled data pairs stemming frommechanical tests under different deformation modes, i.e., uniaxialcompression/tension and simple torsion, and can thus be interpreted as asupervised counterpart to the originally proposed EUCLID that is informed byfull-field displacement data and global reaction forces. The proposed method isverified on synthetical data with artificial noise and validated onexperimental data acquired through mechanical tests of human brain specimens,proving that the method is capable of discovering hyperelastic models thatexhibit both high fitting accuracy to the data as well as concise and thusinterpretable mathematical representations.

Automated discovery of interpretable hyperelastic material models for human brain tissue with EUCLID