Verification and Validation in Scientific Computing
By William Oberkampf, Christopher J. Roy. 2010
1,073 citations recorded by the source corpus.
Subfields: Accounting, Political Science and International Relations
View Paper: https://www.semanticscholar.org/paper/1e6dbf177755a607d1a6a30ed76b847d6e8eef87
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In Political Science and International Relations
Citations in the corpus
- Stable Generalized Finite Element Method (SGFEM) (2011, background): “Validation is the process of building confidence on the mathematical model ([1, 42, 37]).”
- Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI (2021, background): “34 Robustness analysis, on the other hand, allows researchers to learn about the results of a given model, and whether they are an artefact of it (eg, due to a poor idealisation) or whether they are related to core features iii We do not deviate from these authors’ interpretation of the reliability…”
- Simulation in the design and operation of manufacturing systems: state of the art and new trends (2019, background): “However, there are a lot of references in literature, which stand out as key references: Kaizer (Kaizer, Heller, and Oberkampf 2015), the textbooks published by Roache (2009), and Oberkampf and Roy (2010).”
- A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing (2011, methodology): “Monte Carlo sampling to propagate aleatory uncertainties through a model (from [1]).”
- Imprecise probabilities in engineering analyses (2013, neutral): “This property often suffices for use in risk analysis and other fields requiring calculations under uncertainty [176,172,194,169].”
- Verification and validation of Large Eddy Simulation of attached cavitating flow around a Clark-Y hydrofoil (2019, methodology): “…developed for engineering applications including the grid onvergence index (GCI) ( Roache, 1998 ), correction factor method CF) ( Stern et al., 2001; Wilson et al., 2004 ), factor of safety (FS) Xing and Stern, 2010, 2011 ) and their variations ( Eça and Hoektra, 2014; Oberkampf and Roy, 2010 ).”
- The need for uncertainty quantification in machine-assisted medical decision making (2019, background): “A notable example is the US support of a nuclear test moratorium since 1992, whereby we annually provide detailed measures of confidence in the safety, security and performance of the nuclear stockpile—guaranteed through virtual testing 1,2 .”
- Bayesian Probabilistic Numerical Methods (2017, neutral): “Nonetheless, the introduction of error is unavoidable and it is the role of the numerical analyst to provide control of this error (Oberkampf and Roy, 2013).”
- Quantification of model uncertainty in RANS simulations: A review (2018, methodology): “[66], this is done by summarizing the posterior variability of the parameters within and in between calibration datasets (called hereafter scenarios) by means of probability boxes (pboxes), commonly used in Bayesian statistics to summarize the joint effect of parametric and epistemic model-form unce”
- Bayesian Solution Uncertainty Quantification for Differential Equations (2013, background): “However, accounting for this type of verification error for the purpose of model inference has proven to be a difficult open problem (Oberkampf and Roy, 2010), causing discretization uncertainty to be often ignored in practice.”
- Best Practices for Scientific Computing (2012, background): “Exactly how they check correctness depends on the researcher’s understanding of the problem at hand [24, 32, 46].”