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ANL/MCS-P1832-0111 Dimensionality Reduction for Uncertainty Quantification of Nuclear Engineering Models
 

Summary: ANL/MCS-P1832-0111
Dimensionality Reduction for Uncertainty Quantification of Nuclear Engineering Models
Oleg Roderick, Zhu Wang, Mihai Anitescu
Argonne National Laboratory, 9700 S. Cass Ave., Argonne, IL 60439
roderick@mcs.anl.gov, wangzhu@vt.edu, anitescu@mcs.anl.gov
INTRODUCTION
The task of uncertainty quantification consists of
relating the available information on uncertainties in the
model setup to the resulting variation in the outputs of the
model. Uncertainty quantification plays an important role
in complex simulation models of nuclear engineering,
where better understanding of uncertainty results in
greater confidence in the model and in the improved
safety and efficiency of engineering projects.
In our previous work, we have shown that the effect
of uncertainty can be approximated by polynomial
regression with derivatives (PRD): a hybrid regression
method that uses first-order derivatives of the model
output as additional fitting conditions for a polynomial
expansion. Numerical experiments have demonstrated the

  

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

 

Collections: Computer Technologies and Information Sciences; Mathematics