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Verification, validation, and predictive capability in computational engineering and physics.
Author(s) -
William L. Oberkampf,
Charles Hirsch,
T.G. Trucano
Publication year - 2003
Publication title -
osti oai (u.s. department of energy office of scientific and technical information)
Language(s) - English
Resource type - Reports
DOI - 10.2172/918370
Subject(s) - verification and validation of computer simulation models , computer science , computation , computational model , verification and validation , confidence interval , model validation , data mining , algorithm , data science , statistics , mathematics
Developers of computer codes, analysts who use the codes, and decision makers who rely on the results of the analyses face a critical question: How should confidence in modeling and simulation be critically assessed? Verification and validation (V&V) of computational simulations are the primary methods for building and quantifying this confidence. Briefly, verification is the assessment of the accuracy of the solution to a computational model. Validation is the assessment of the accuracy of a computational simulation by comparison with experimental data. In verification, the relationship of the simulation to the real world is not an issue. In validation, the relationship between computation and the real world, i.e., experimental data, is the issue

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