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Virtual Measurement

The Virtual Measurement Systems Program introduces metrology constructs - standard reference computations, uncertainty quantification, and traceability - into scientific computation and computer-assisted measurement technologies. As with physical measurement systems, development of a virtual metrology infrastructure will result in predictive computing with quantified reliability. In turn, this will enable improved decision making based on results of computer simulations.


Computing has become an indispensable component of modern science and engineering research. As has been repeatedly observed and documented, the modern computer is many orders of magnitude more powerful than its early predecessors, capable of simulating physical problems of unprecedented complexity.

Given the success of scientific computation as a research tool, it is natural that scientists, engineers, and policy makers strive to harness this immense potential by using computational models for critical decision-making. Increasingly, computers are being used to supplement experiments, to prototype engineering systems, or to predict the safety and reliability of high-consequence systems. Such use inevitably leads one to question "How good are these simulations?" Unfortunately, most computational scientists today are ill equipped to address such important questions with the same scientific rigor that is routine in experimental science.

In broad terms, uncertainty quantification strives to provide a quantitative characterization of the quality of a numerical simulation.  In practice, this activity represents a large and growing body of approaches to define, characterize, propagate, infer, and communicate the diverse forms of incomplete and imperfect knowledge that enter into and affect the output of computational models.  The similarity between this activity and classical measurement science constructs motivates the investigation of numerical simulation as a new form of measurement in silico.


Standard Reference Computations: Reference implementations of numerical solvers, benchmark problems, numerical reference data. Uncertainty Quantification for Models and Simulation:  Applications of uncertainty quantification to numerical simulation results in select scientific fields.

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Related Links

Materials Genome Initiative at NIST
Virtual Measurement Systems, NIST Computational Chemistry Group
Managing Uncertainty of Computational Models, University of Sheffield
Activity Group on Uncertainty Quantification, SIAM

For additional links see here

Conferences and Events

International Conference on Special Functions in the 21st Century: Theory and Applications, April 6-8, 2011, Washington D. C.

IFIP Working Conference on Uncertainty Quantification in Scientific Computing: August 1-4, 2011, Boulder, Colorado

For additional conferences and events see here

Staff Directory

Andrew Dienstfrey
Program Manager
303-497-7485 Telephone

325 Broadway, M/S 891.01
Boulder, CO  80305-3328