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Verification and Validation (V&V):
Quantifying Prediction Uncertainty and Demonstrating Simulation
Credibility
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Event Type:Webinar
Location: Online,USA
Date: May 15, 2008
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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.)
Quantifying Prediction Uncertainty and Demonstrating Simulation
Credibility
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.
Agenda
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
Closing
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.
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