Identification of model parameters through calibration processes is critical to achieve realistic results in computational mechanics models. Identifying these parameters from observed experimental data represents an inverse problem. With advances in measurement technology, more experimental data is available to calibrate numerical models (e.g. digital image correlation [DIC]); at the same time, computational models tend to increase in complexity over time, making a rigorous calibration process more difficult. Identifying model parameters is further complicated in the presence of highly nonlinear, unstable, and stochastic physical phenomena considered by computational mechanics models, such as turbulence or fracture. Model calibration is particularly critical for constitutive modeling in solid mechanics but retains application to diverse physics. A range of techniques have been applied to perform model calibration in computational mechanics, such as gradient-based optimization methods, non-gradient and genetic optimization methods, adjoint methods, and probabilistic (e.g. Bayesian) methods. A new class of models employ machine-learning techniques, such as surrogate models or physical-informed neural networks, for model parametrization. Yet, significant challenges remain for model calibration: these methods can be prohibitively time consuming when applied to high-frequency or full-field experimental data, or to models with many parameters; calibrated parameter sets must be confined to physical spaces and achieve stable solution (e.g. Courant-Friedrichs-Lewy [CFL] condition) when applied to problems distinct from the characterization data. For this minisymposium, we solicit contributions that address these challenges to advance the state of the field, namely those that (1) propose novel methods for calibration that overcome these shortcomings, (2) demonstrate rigorous application of calibration processes on complex datasets (e.g. high-frequency or full-field data), or (3) validate, assess, or critique calibration methods on distinct applications. Contributions from solid mechanics, fluid mechanics, and other physics are welcomed.