Abstract

We present two reduced-order model based approaches  for the efficient and accurate evaluation of the Conditional-Value-at-Risk  (CVaR) of quantities of interest (QoI) in engineering systems with uncertain parameters.  CVaR is used to model objective or constraint functions in risk-averse engineering design and optimization applications under uncertainty.  Estimating the CVaR of the QoI is expensive. While the distribution of the uncertain system parameters is known, the resulting QoI is a random variable that is implicitly determined via the state of the system. Evaluating the CVaR of the QoI requires  sampling in the tail of the QoI distribution and typically requires  many solutions of an expensive full-order model of the engineering system. Our reduced-order model approaches substantially reduce this computational expense.