  This work proposes a mathematical framework to increase the robustness to
rare events of digital twins modelled with graphical models. We incorporate
probabilistic model-checking and linear programming into a dynamic Bayesian
network to enable the construction of risk-averse digital twins. By modeling
with a random variable the probability of the asset to transition from one
state to another, we define a parametric Markov decision process. By solving
this Markov decision process, we compute a policy that defines state-dependent
optimal actions to take. To account for rare events connected to failures we
leverage risk measures associated with the distribution of the random variables
describing the transition probabilities. We refine the optimal policy at every
time step resulting in a better trade off between operational costs and
performances. We showcase the capabilities of the proposed framework with a
structural digital twin of an unmanned aerial vehicle and its adaptive mission
replanning.
