From data to reality: Criteria for the validation of data-driven models for history-dependent materials

This paper addresses the issue of validating identified data-driven material models for history-dependent materials, which are nowadays typically represented using neural networks. For this purpose, we introduce an a posteriori general acceptability criterion based on the Constitutive Relation Error (CRE). A computed solution using the identified material model is deemed acceptable if it closely approximates the experimental data when using the CRE metric.