Sparse regression and feature extraction are the cornerstones of knowledge discovery from
massive data. Their goal is to discover interpretable and predictive models that provide
simple relationships among scientific variables. While the statistical tools for model
discovery are well established in the context of linear regression, their generalization to
nonlinear regression in material modeling is highly problem‐specific and insufficiently
understood. Here we explore the potential of neural networks for automatic model discovery …