Optimal uncertainty quantification (OUQ) is a framework for numerical extreme-case analysis of stochastic systems with imperfect knowledge of the underlying probability distribution. This paper presents sufficient conditions under which an OUQ problem can be reformulated as a finite-dimensional convex optimization problem, for which efficient numerical solutions can be obtained. The sufficient ...

Proper uncertainty quantification requires the construction of a large set of alternative Earth models. To overcome computational time, distance-based techniques have been recently used in the context of uncertainty quantification. However, they rely on the construction of hundreds of models, prior to the study. One difficulty is that there is no information a priori on the number of models tha...

Abstract An artificial neural network based reduced order model (ROM) is developed to predict the load coefficients and performance of wind turbine airfoils. The trained using a representative database 972 airfoil shapes generated by perturbing design parameters in each 12 baseline airfoils defining commercially relevant modern turbines. predictions from our ROM show excellent agreement with CF...

For both scientific and policy reasons, there is increasing demand for quantification of variability and uncertainty in emission factors and inventories. Variability refers to inherent differences in emissions among different sources or for a given source over time. Uncertainty refers to lack of knowledge regarding the true value of emissions at a given location and time period. Scientific advi...