Uncertainty Quantification (UQ) in generic MonteCarlo simulations

We present results from a recently launched project to study computational issues related to the quantification of non statistical uncertainties in numerical ( Monte Carlo) simulations: they derive from different areas of the process of simulation[1], like e.g. epistemic uncertainties[2], experimental errors in physical data, error propagation from the employed numerical algorithms, etc.. This paper addresses the development of methods to predict the effects of a set of correlated, partially correlated or un-correlated physical uncertainties on the observables produced in a Monte Carlo simulation. It also provides some insight on the computational effort needed and on the possible software solutions to be implemented in the kernel of Monte Carlo codes to facilitate the quantification of uncertainty in experimental use cases.