A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing

Roy, Christopher J.; Oberkampf, William L.

doi:10.1016/j.cma.2011.03.016

Cited by 605 publications

(399 citation statements)

References 28 publications

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“…Validation is assessment of model accuracy by way of comparison of simulation results with experimental measurements [2]. Thus, we used some experimental data offered by NASA Langley [13] and other researchers to validate our model.…”

Section: Model Validationmentioning

confidence: 99%

“…The characterization of the numerical approximation errors associated with a simulation is called verification [1]. In other words, verification is concerned with whether the code is solving the chosen equations correctly and error-free while the validation deals with physics and addresses the appropriateness of the model [2].…”

Section: Introductionmentioning

confidence: 99%

“…3) Numerical errors estimation (discretization error, iterative convergence error, and round-off error) [4]. Roy and William [2] have proposed study included new concepts for estimating uncertainties due to numerical errors. Roy [4], [9] has carried out investigation of code and solution verification methods and estimating discretization error.…”

Section: Introductionmentioning

confidence: 99%

“…The second one is the solution verification, which is the process of qualifying the numerical errors that occur in the numerical simulation. These numerical errors are, round-off error, iterative error, discretization error, and errors due to computer programming mistakes [2], [3].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Verification Study of a CFD-RANS Code for Turbulent Flow at High Reynolds Numbers

Musa¹

2016

IJMO

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Section: Model Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Verification Study of a CFD-RANS Code for Turbulent Flow at High Reynolds Numbers

Musa¹

2016

IJMO

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“…However, it requires complete probability information for the random process, which could be quite difficult, if not impossible. Modeling epistemic uncertainty, referring to the uncertainty due to lack of knowledge [20], requires the exploration of the alternatives of probabilistic approaches. In the past few decades, possibility theory [6] and Dempster-Shafer (DS) theory [21], have been explored and studied in the uncertainty quantification literature for a better representation of the epistemic uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Application of Uncertainty Modeling Frameworks to Uncertain Isosurface Extraction

Mirzargar

Kirby

2015

Lecture Notes in Computer Science

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Abstract. Proper characterization of uncertainty is a challenging task. Depending on the sources of uncertainty, various uncertainty modeling frameworks have been proposed and studied in the uncertainty quantification literature. This paper applies various uncertainty modeling frameworks, namely possibility theory, Dempster-Shafer theory and probability theory to isosurface extraction from uncertain scalar fields. It proposes an uncertainty-based marching cubes template as an abstraction of the conventional marching cubes algorithm with a flexible uncertainty measure. The applicability of the template is demonstrated using 2D simulation data in weather forecasting and computational fluid dynamics and a synthetic 3D dataset.

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Validation of sea ice models using an uncertainty‐based distance metric for multiple model variables

et al. 2017

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We implement a variance‐based distance metric (Dn) to objectively assess skill of sea ice models when multiple output variables or uncertainties in both model predictions and observations need to be considered. The metric compares observations and model data pairs on common spatial and temporal grids improving upon highly aggregated metrics (e.g., total sea ice extent or volume) by capturing the spatial character of model skill. The Dn metric is a gamma‐distributed statistic that is more general than the χ2 statistic commonly used to assess model fit, which requires the assumption that the model is unbiased and can only incorporate observational error in the analysis. The Dn statistic does not assume that the model is unbiased, and allows the incorporation of multiple observational data sets for the same variable and simultaneously for different variables, along with different types of variances that can characterize uncertainties in both observations and the model. This approach represents a step to establish a systematic framework for probabilistic validation of sea ice models. The methodology is also useful for model tuning by using the Dn metric as a cost function and incorporating model parametric uncertainty as part of a scheme to optimize model functionality. We apply this approach to evaluate different configurations of the standalone Los Alamos sea ice model (CICE) encompassing the parametric uncertainty in the model, and to find new sets of model configurations that produce better agreement than previous configurations between model and observational estimates of sea ice concentration and thickness.

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A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing

Cited by 605 publications

References 28 publications

Verification Study of a CFD-RANS Code for Turbulent Flow at High Reynolds Numbers

Verification Study of a CFD-RANS Code for Turbulent Flow at High Reynolds Numbers

Application of Uncertainty Modeling Frameworks to Uncertain Isosurface Extraction

Validation of sea ice models using an uncertainty‐based distance metric for multiple model variables

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