Model verification, validation, and uncertainty quantification (VVUQ) is a methodology for the development of numerical models that can be used to make predictions with quantified accuracy and confidence. As defined in [1], verification is the process of determining that a computational model accurately represents the underlying mathematical model and its solution. Validation is the process of determining the degree to which a model is an accurate representation of the validation experiments from the perspective of the intended uses of the model. Uncertainty quantification (UQ) in the context of VVUQ is the mathematical assessment of uncertainties in model simulation results and experimental results. Quantifying the predictive accuracy of a model provides the decision-maker or stakeholder with information necessary for making risk-informed decisions. The fundamental approach of VVUQ is one of accumulating evidence to quantify the accuracy of the computer model for specified conditions. A key step in VVUQ is the comparison of model predictions and experimental data given uncertainties in both. Because uncertainties—both random and epistemic—exist in nature and in our ability to test and measure, uncertainties must also be considered in corresponding model simulations and predictions. This step is critical because it defines how the model performs against the physical data, which are taken as the ground truth. The output of a VVUQ effort should be, for example, measures such as model accuracy and associated confidence in terms of percent probability and error. While UQ is included in [1], the need for additional detail on definitions, procedures and examples of UQ motivated the publication of [2]. This presentation will focus on the application of UQ methods with a focus on the comparison of uncertain simulation and experimental results. REFERENCES [1] ASME VV-10:2019, Standard for Verification and Validation in Computational Solid Mechanics. [2] ASME VVUQ-10.2:2021, The Role of Uncertainty Quantification in Verification and Validation of Computational Solid Mechanics Models.