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April 9, 2012
How do you measure uncertainty?
Donald Estep, University Interdisciplinary Research Scholar and professor in the Department of Statistics, has been appointed as founding co-editor in chief of a new Journal of Uncertainty Quantification to be offered as a joint publication of the Society for Industrial and Applied Mathematics and the American Statistical Association.
In very broad terms, Uncertainty Quantification in computational science and engineering involves describing effects of error and uncertainty on simulations and predictions of the behavior of complex systems arising in, for example, physics, biology, chemistry, ecology, engineered systems and even politics.
Science and engineering increasingly rely on simulations of mathematical models to supplement – or even replace – experimental observation. However, results from mathematical modeling are subject to errors and uncertainty emanating from a variety of sources, including uncertainty in data obtained from experiment and observation, limitations of physical modeling, problems in computer codes and the difficulty of combining models into integrated systems.
“Quantifying the effects of these uncertainties is crucial to bringing to fruition the dream of being able to accurately model and predict real complex processes through computational simulators,” Estep said. “Uncertainty Quantification involves tackling a wide range of mathematical and statistical research of great technical difficulty, which is why my colleagues and I proposed this new journal – a rare achievement in the world of scholarly journals”
In 2010, Estep, Professor Jim Berger of Duke University and Professor Max Gunzburger of Florida State University proposed the creation of a new journal to address the major challenges of Uncertainty Quantification.
The new journal will be the first to be jointly offered by the Society for Industrial and Applied Mathematics and the American Statistical Association. The Journal of Uncertainty Quantification will contain research articles presenting significant mathematical, statistical, algorithmic and application advances in Uncertainty Quantification. A key goal of the journal will be nurturing interactions between the mathematical, statistical, computational and applications communities involved in uncertainty quantification and related areas.
Articles for JUQ are now invited and should be submitted at the journal website at www.siam.org/journals/juq.php.
Contact: Emily Wilmsen
Phone: (970) 491-2336