Verification and Validation (V&V): Quantifying Prediction Uncertainty and Demonstrating Simulation Credibility

NAFEMS Webinar Series

Verification and Validation (V&V):Quantifying Prediction Uncertainty and Demonstrating Simulation Credibility

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(Note: This broadcast is part of the NAFEMS vendor series that allows various solutions providers the opportunity to deliver technical information to the NAFEMS community. NAFEMS does not endorse any vendor, but tries to provide an unbiased view of the marketplace.)

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.


Welcome & Introduction
Matthew Ladzinski, NAFEMS North America

Verification and Validation (V&V): Quantifying Prediction Uncertainty and Demonstrating Simulation Credibility Dr. François M. Hemez, Los Alamos National Laboratory

Q & A Session



Related White Papers:

Webinar attendees will receive the following white papers authored by Dr. François M. Hemez:


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Webinar Presenter

Dr. François M. Hemez

Ph.D., Aerospace Engineering, University of Colorado at Boulder, Colorado, 1993.

M.S., Aerospace Engineering, University of Colorado at Boulder, Colorado, 1990.

Graduate degree in numerical analysis, Université Pierre et Marie Curie (Jussieu), France, 1989.

Graduate degree in engineering, École Centrale de Paris, France, 1989.

François Hemez has been Technical Staff Member at the Los Alamos National Laboratory (LANL) since 1997, where he is recognized for his expertise as a scientist and ongoing contributions to the Laboratory programmatic missions. He is contributing to the development of technology for solution verification, model validation, uncertainty quantification, and decision-making for engineering and weapon physics applications. He has been intimately involved in developing, building, and utilizing theoretical, algorithmic, and computational tools that are directly applicable to high-consequence Laboratory activities. François Hemez has co-developed a short course on the Verification and Validation (V&V) of computational models and taught the first-ever graduate course offered in a U.S. University on uncertainty quantification and V&V (University of California San Diego, Spring 2006). He is serving as chair of the Society for Experimental Mechanics (SEM) technical division on model validation and uncertainty quantification (2005-2009) and is an elected member of the SEM executive board (2007-2009). In 2005 he received the Junior Research Award of the European Association of Structural Dynamics for his contribution to test-analysis correlation. In 2006 he received two U.S. Department of Energy Defense Programs Awards of Excellence for applying V&V to programmatic work at LANL. Dr. Hemez has a proven scientific record (over 200 publications, including 22 peer-reviewed papers) in a broad spectrum of computational science areas, including theoretical, computational, and experimental dynamics, computational physics, verification and validation, probability and statistics, uncertainty quantification, and information theory.