ORTHOGONAL BASES FOR POLYNOMIAL REGRESSION WITH DERIVATIVE INFORMATION IN UNCERTAINTY QUANTIFICATION

@article{Li2010ORTHOGONALBF,
  title={ORTHOGONAL BASES FOR POLYNOMIAL REGRESSION WITH DERIVATIVE INFORMATION IN UNCERTAINTY QUANTIFICATION},
  author={Yiou Li and M. Anitescu and O. Roderick and F. J. Hickernell},
  journal={International Journal for Uncertainty Quantification},
  year={2010},
  volume={1},
  pages={297-320}
}
We discuss the choice of polynomial basis for approximation of uncertainty propagation through complex simulation models with capabilityto outputderivative information.Ourwork ispart of a larger research effortinuncertaintyquantification using sampling methods augmented with derivative information. The approach has new challenges compared with standard polynomial regression. In particular, we show that a tensor product multivariate orthogonal polynomial basis of an arbitrary degree may no… Expand
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