Verification and Validation (V&V) refers to a broad range of activities that are carried out to provide evidence that measurements and predictions are credible and scientifically defendable.

This presentation offers an introduction to the main concepts of V&V and lessons learned after fifteen years of research, development, and application of V&V technology at the Los Alamos National Laboratory (LANL).

The discussion is somewhat restricted to Structural Dynamics even though V&V at LANL reaches across software quality assurance, verification, data analysis and archiving, engineering simulation, computational physics and astrophysics simulation, and the quantification of uncertainty. While high-level concepts are emphasized, references are made available for the implementation of specific tools or application case studies.

The cornerstone of V&V is threefold with, first, showing whenever possible that predictions of numerical simulations are accurate relative to test data over a range of settings or operating conditions; second, quantifying the sources and levels of prediction uncertainty; and, third, demonstrating that predictions are robust, that is, insensitive, to the modeling assumptions and lack-of-knowledge.