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

Published

Author(s)

David A. Sheen

Abstract

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.
Citation
Journal of Research (NIST JRES) -
Volume
122

Keywords

experimental database, experimental design, optimization, outlier detection, uncertainty analysis.

Citation

Sheen, D. (2017), mumpce_py: A Python implementation of the Method of Uncertainty Minimization using Polynomial Chaos Expansions, Journal of Research (NIST JRES), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/jres.122.039 (Accessed May 28, 2024)

Issues

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Created October 4, 2017, Updated November 10, 2018