For the parameter identification an expression tree with three layers, including the shear rate The obtained portfolio of models, Table , initially consisted of five models. As the complexity is constrained by an upper bound (inequality), similar models with the same complexity are identified. These, together with invariant models at higher complexities, were neglected. To choose the best model among the identified ones, the prediction of the models was plotted together with the experimental data, see Figure , and the training and extrapolation errors were compared. As expected, the training error decreases with complexity of the model as there is more flexibility allowed to SR. However, the comparison of the extrapolation errors shows the superiority of Newton's law model (𝐶 = 3), whereas the other identified models suffer from overfitting. Overall, the Newton's law model can be selected as the sparsest model with the highest generalisation capability, and can be easily interpreted to generate knowledge about the physics of the system under investigation. The model identification was conducted with only ten data points, highlighting the sparsity of required data points of the presented method compared to other data-driven methods. This is especially beneficial in chemistry, where data points can be expensive to generate.