LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

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

Photo from wikipedia

The Method of Uncertainty Minimization using Polynomial Chaos Expansions (MUM-PCE) [1-6] was developed as a software tool to constrain physical models against experimental measurements. These models contain parameters that cannot… Click to show full abstract

The Method of Uncertainty Minimization using Polynomial Chaos Expansions (MUM-PCE) [1-6] 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. MUM-PCE was written to provide a streamlined workflow for computational uncertainty analysis. The software provides the following functionality:

Keywords: uncertainty minimization; uncertainty; using polynomial; method uncertainty; minimization using; polynomial chaos

Journal Title: Journal of Research of the National Institute of Standards and Technology
Year Published: 2017

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.