Cardiovascular system dynamics have long been studied using mathematical models ranging from simple algebraic models, over compartment models to 3D fluiddynamics and mechanics models. With the uptake in availability of data, the need for predictive models and the development of digital twins is rising. This talk will provide an overview of some of the many sources of uncertainty in these models. We compare use results from sensitivity analysis for asymptotic prediction of confidence and prediction intervals with intervals determined using Bayesian sampling. We also address how to incorporate model mismatch using Gaussian Processes and demonstrate the physics based neural network model. These topics will be demonstrated in a closed loop model used to understand the importance of ventricular-ventricular interaction in patients with pulmonary hypertension and in a 1D fluid dynamics network model developed to study what vessels to open in patients with chronic thromboembolic pulmonary hypertension.