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Assessing the Reliability of Complex Models - Mathematical and Statistical Foundations of Verification, Validation, and Uncertainty Quantification (Paperback)
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Assessing the Reliability of Complex Models - Mathematical and Statistical Foundations of Verification, Validation, and Uncertainty Quantification (Paperback)
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Advances in computing hardware and algorithms have dramatically
improved the ability to simulate complex processes computationally.
Today's simulation capabilities offer the prospect of addressing
questions that in the past could be addressed only by
resource-intensive experimentation, if at all. Assessing the
Reliability of Complex Models recognizes the ubiquity of
uncertainty in computational estimates of reality and the necessity
for its quantification. As computational science and engineering
have matured, the process of quantifying or bounding uncertainties
in a computational estimate of a physical quality of interest has
evolved into a small set of interdependent tasks: verification,
validation, and uncertainty of quantification (VVUQ). In
recognition of the increasing importance of computational
simulation and the increasing need to assess uncertainties in
computational results, the National Research Council was asked to
study the mathematical foundations of VVUQ and to recommend steps
that will ultimately lead to improved processes. Assessing the
Reliability of Complex Models discusses changes in education of
professionals and dissemination of information that should enhance
the ability of future VVUQ practitioners to improve and properly
apply VVUQ methodologies to difficult problems, enhance the ability
of VVUQ customers to understand VVUQ results and use them to make
informed decisions, and enhance the ability of all VVUQ
stakeholders to communicate with each other. This report is an
essential resource for all decision and policy makers in the field,
students, stakeholders, UQ experts, and VVUQ educators and
practitioners. Table of Contents Front Matter Summary 1
Introduction 2 Sources of Uncertainty and Error 3 Verification 4
Emulation, Reduced-Order Modeling, and Forward Propagation 5 Model
Validation and Prediction 6 Making Decisions 7 Next Steps in
Practice, Research, and Education for Verification, Validation, and
Uncertainty Quantification Appendixes Appendix A: Glossary Appendix
B: Agendas of Committee Meetings Appendix C: Committee Biographies
Appendix D: Acronyms
General
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