Being Sensitive to Uncertainty

Authors:
Leon M. Arriola;James M. Hyman
Affiliations:
University of Wisconsin-Whitewater;Los Alamos National Laboratory
Venue:
Computing in Science and Engineering
Year:
2007

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Abstract

Predictive modeling's effectiveness is hindered by inherent uncertainties in the input parameters. Sensitivity and uncertainty analysis quantify these uncertainties and identify the relationships between input and output variations, leading to the construction of a more accurate model. This survey introduces the application, implementation, and underlying principles of sensitivity and uncertainty quantification.