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The Masters of Uncertainty

HPC Wire (09/13/13) Nicole Hemsoth

The California Institute of Technology’s Houman Owhadi and Clint Scovel recently spoke with HPC Wire about Bayesian methods and the role of uncertainty in supercomputing. Bayesian inference allows researchers to test the outcomes of interest by modeling uncertainty combined with some prior data. Uncertainty quantification is especially useful with high-performance computing, as more advanced computers enable researchers to compute the Bayesian posterior, or the conditional probability of an uncertainty that is assigned after known evidence is considered, which previously could not be calculated. Fields such as risk analysis and climate modeling can particularly benefit from uncertainty quantification. For example, Boeing must certify that new airplane models have a probability of catastrophic event that is less than 10 to the power of minus nine per hour of flight. To perform safety assessments, Boeing cannot fly a billion airplanes to determine how many crash, Owhadi says, so the company takes limited data and processes it in an optimal way to predict risk. Owhadi notes that he and other researchers are developing an algorithmic framework that enables optimal processing of information. “We’re saying we want to formulate this problem that we’re trying to solve and we’re going to use our computing capability–in particular, high-performance computing–to solve these problems,” Scovel says.

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