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International Journal for Uncertainty Quantification 1(1), xxxxxx (2010) Preprint ANL/MCS-P1806-1110
 

Summary: International Journal for Uncertainty Quantification 1(1), xxx­xxx (2010)
Preprint ANL/MCS-P1806-1110
ORTHOGONAL BASES FOR POLYNOMIAL REGRES-
SION WITH DERIVATIVE INFORMATION IN UNCER-
TAINTY QUANTIFICATION
Yiou Li1
, Mihai Anitescu2,
, Oleg Roderick2
& Fred Hickernell1
1
Department of Applied Mathematics, Illinois Institute of Technology, 10 W. 32nd, Chicago, IL,
60616, USA
2
Mathematics and Computer Science Division, Argonne National Laboratory, 9600 S. Cass Ave.,
Argonne, IL, 60439, USA
Original Manuscript Submitted: 11/08/2010; Final Draft Received: 11/08/2010
We discuss the choice of polynomial basis for approximation of uncertainty propagation through complex simulation
models with capability to output derivative information. Our work is part of a larger research effort in uncertainty
quantification 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

  

Source: Anitescu, Mihai - Mathematics and Computer Science Division, Argonne National Laboratory

 

Collections: Mathematics