Daniel P. Giesy

Learn More
This article presents a unifying framework to uncertainty quantification for systems subject to several design requirements that depend polynomially on both aleatory and epistemic uncertainties. This methodology, which is based on the Bernstein expansions of polynomials, enables calculating bounding intervals for the range of means, variances and failure(More)
— This paper develops techniques for constructing empirical predictor models based on observations. By contrast to standard models, which yield a single predicted output at each value of the model's inputs, Interval Predictors Models (IPM) yield an interval into which the unobserved output is predicted to fall. The IPMs proposed prescribe the output as an(More)
  • 1