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mumpce_py: A Python implementation of the Method of Uncertainty Minimization using Polynomial Chaos Expansions



David A. Sheen


The Method of Uncertainty Minimization using Polynomial Chaos Expansions (MUM-PCE) was developed as a software tool to constrain physical models against experimental measurements. These models contain parameters that cannot be easily determined from first principles and so must be measured, and some which cannot even be easily measured. In such cases, the models are validated and tuned against a set of global experiments which may depend on the underlying physical parameters in a complex way. The measurement uncertainty will affect the uncertainty in the parameter values.
Journal of Research (NIST JRES) -


experimental database, experimental design, optimization, outlier detection, uncertainty analysis.
Created October 4, 2017, Updated November 10, 2018