Modern predictive simulations have a special focus on the systematic treatment of input, model and data uncertainties and their propagation through a computational model to produce predictions of Quantities-of-Interest (QoIs) with quantified uncertainty. Although the modeling of nuclear reactors has made tremendous progress, there are always discrepancies between ideal in silico designed systems and real-world manufactured ones. As a consequence, uncertainties must be quantified along with simulation to facilitate optimal design and decision making, ensure robustness, performance and safety margins. This workshop will provide an overview of the fundamental concepts in Uncertainty Quantification (UQ) and Sensitivity Analysis (SA), as well as comparative reviews of forward/inverse UQ and SA approaches. Topics on quantifying prediction uncertainties in Machine Learning models will also be briefly covered.

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