The integration of measurements and simulations is increasingly used to improve the understanding of mechanical testing results and provides a means to advance the mechanical testing paradigm toward fewer, more complex tests that increase throughput and provide rich datasets that can be exploited by machine learning and artificial intelligence. Current advanced mechanical testing activities involve three-dimensional surface digital image correlation (DIC) datasets with fully three-dimensional finite element analysis (FEA) results in mechanical test setups on metals and polymers including uni-axial and bi-axial loading at different strain rates and temperatures. Identifying material properties and constitutive model parameters from the integrated data sets currently focuses on the finite element model updating (FEMU) method, but there is interest in expanding to include inverse methods based on model-inspired analysis of experimental data by itself. Additional data streams from infrared imaging and diffraction stress measurements further enriches the available data from which material behavior can be extracted. Separate work is being done to develop robust algorithms to quantitatively compare the physical and simulated experimental results. This opportunity focuses on the use of inverse methods to determine material properties and key constitutive model parameters, and to develop new material-dependent test strategies to deliver more diverse information from fewer experiments.