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title = {Dakota, A Multilevel Parallel Object-Oriented Framework
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for Design Optimization, Parameter Estimation, Uncertainty
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Quantification, and Sensitivity Analysis: Version 6.6
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Theory Manual},
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year = {2017}
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@article{adams2019dakota,
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author = {Adams, Brian M and Eldred, Michael S and Geraci, Gianluca and Hooper, Russell W and Jakeman, John D and Maupin, Kathryn A and Monschk, Jason A and Rushdi, Ahmad A and Stephens, J Adam and Swiler, Laura P and Wildey, Timothy M and Bohnhoff, William J and Dalbey, Keith R and Ebeida, Mohamed S and Eddy, John P and Hough, Patricia D and Khalil, Mohammad and Hu, Kenneth T and Ridgway, Elliott M and Vigil, Dena M and Winokur, Justin G},
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title = {{Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.10 User{\textquoteright}s Manual}},
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journal = {Sandia National Laboratories, Tech. Rep. SAND2014-4633},
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@@ -31,7 +31,7 @@ affiliations:
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- name: University of Colorado at Boulder, Institute for Arctic and Alpine Research
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index: 4
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date: 4 September 2019
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bibliography: papers.bib
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bibliography: paper.bib
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---
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# Summary
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Umami offers two primary classes: a [`Residual`](https://umami.readthedocs.io/en/latest/umami.residual.html#Residual)
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which represents the difference between model and data, and a [`Metric`](https://umami.readthedocs.io/en/latest/umami.metric.html)
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which is a calculated value on either model or data. The set of currently supported calculations are found in the [`umami.calculations`](https://umami.readthedocs.io/en/latest/umami.calculations.html) submodule. Both the `Metric` and `Residual` classes are designed to be fully specified through a YAML-style input-file or python Dictionary interface. Many different calculations can be accomplished through parameter specification. This supports reproducible analysis and systematic variation in metric construction. For example, when used with `terrainbento` one input file can describe the model run, and one input file can describe the model assessment or model-data comparison. This streamlines model analysis applications by making driver files more re-usable and by placing the code that accomplished calculations in the umami package rather than within the driver file. Umami also provides multiple output formats (YAML and Dakota), the latter of which is designed to interface with Sandia National Laboratory's Dakota package [@adams2017dakotatheory].
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which is a calculated value on either model or data. The set of currently supported calculations are found in the [`umami.calculations`](https://umami.readthedocs.io/en/latest/umami.calculations.html) submodule. Both the `Metric` and `Residual` classes are designed to be fully specified through a YAML-style input-file or python Dictionary interface. Many different calculations can be accomplished through parameter specification. This supports reproducible analysis and systematic variation in metric construction. For example, when used with `terrainbento` one input file can describe the model run, and one input file can describe the model assessment or model-data comparison. This streamlines model analysis applications by making driver files more re-usable and by placing the code that accomplished calculations in the umami package rather than within the driver file. Umami also provides multiple output formats (YAML and Dakota), the latter of which is designed to interface with Sandia National Laboratory's Dakota package [@adams2019dakota].
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The novel contribution of the umami package is not primarily found in the specific calculations accomplished (e.g., some of them are as straightforward as the mean of a state variable). Instead it is the flexible and extensible nature of the input file format and the `Metric` and `Residual` classes. Additionally, the package can be extended through the addition of new calculation methods.
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