The realistic prediction of material behavior using computer-aided methods is essential for many engineering applications. This involves the formulation and algorithmic implementation of suitable mathematical constitutive models but, just as importantly, their calibration and validation based on experimental characterization data. Classically, the data required for the identification of model parameters is obtained through relatively simple (uniaxial; measurement of homogeneous gauge length response), standardized, macroscale experiments. More recently, the emergence of ever more complex material systems, but simultaneously much higher-fidelity experimental techniques, has offered novel opportunities, but also posed new challenges for experimentalist and material modelers alike. In the field of experimental methods such developments have included the use of specimens with heterogeneous stress and strain fields, the measurement of strain fields on the specimen surfaces by digital image correlation or the acquisition of microstructural data, e.g., by stacked microscope images or micro-CT scans. Current research is focused on integrating experiments and simulations, in a way that one discipline can inform the other. Examples include data-driven and hybrid modeling approaches and, conversely, the simulation-supported design and interpretation of experiments. Modern approaches further aim to enable ontology-based interoperability across heterogeneous data, in which information from different experimental techniques but also from numerical experiments in virtual laboratories is able to co-exist and mutually inform. This interdisciplinary Minisymposium brings together experts interested in advancing research at the intersection of experimental and computational mechanics.