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International Journal for Uncertainty Quantification
IF: 0.967 5-Year IF: 1.301 SJR: 0.531 SNIP: 0.8 CiteScore™: 1.52

ISSN Print: 2152-5080
ISSN Online: 2152-5099

Open Access

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.v2.i1.60
pages 73-94


Chandrika Kamath
Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California, 94551, USA


Techniques from scientific data mining are increasingly being used to analyze and understand data from scientific observations, simulations, and experiments. These methods provide scientists the opportunity to automate the tedious manual processing of the data, control complex systems, and gain insights into the phenomenon being modeled or observed. This process of data-driven scientific inference borrows ideas and solutions from a range of fields including machine learning, image and video processing, statistics, high-performance computing, and pattern recognition. The tasks involved in these analyses include the extraction of structures from the data, the identification of representative features for these structures, dimension reduction, and building predictive and descriptive models. At first glance, data mining and data-driven analysis may appear unrelated to stochastic modeling and uncertainty quantification. But, as we show in this paper, there are commonalities in the problems addressed and techniques used, providing the two communities the opportunity to benefit from the expertise and experiences of each other.