Abstract Herein, an artificial neural network (ANN)-based approach for the efficient automated modeling and simulation of isotropic hyperelastic solids is presented. Starting from a large data set comprising deformations corresponding stresses, simple, physically based reduction problem’s dimensionality performed in processing step. More specifically, three deformation type invariants serve as ...