mumpcepy: A Python implementation of the Method of Uncertainty Minimization using Polynomial Chaos Expansions
Description
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.
Resources
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A repository containing the Python source code for the mumpce_py software. This software can be downloaded onto any machine with a Python interpreter or it can be cloned from GitHub. |
https://github.com/usnistgov/mumpce_py |
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https://doi.org/10.18434/M3WT1B |
Tags
- experimental-database-experimental-design-optimization-outlier-detection-uncertainty-analysis