  This review article highlights state-of-the-art data-driven techniques to
discover, encode, surrogate, or emulate constitutive laws that describe the
path-independent and path-dependent response of solids. Our objective is to
provide an organized taxonomy to a large spectrum of methodologies developed in
the past decades and to discuss the benefits and drawbacks of the various
techniques for interpreting and forecasting mechanics behavior across different
scales. Distinguishing between machine-learning-based and model-free methods,
we further categorize approaches based on their interpretability and on their
learning process/type of required data, while discussing the key problems of
generalization and trustworthiness. We attempt to provide a road map of how
these can be reconciled in a data-availability-aware context. We also touch
upon relevant aspects such as data sampling techniques, design of experiments,
verification, and validation.
