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Computational Sciences & Mathematics

Uncertainty Quantification

PNNL enables probabilistic and mathematical models to represent and explore stochastic processes and phenomena, especially when lab experiments are too costly, difficult, hazardous, or time-consuming. Empirical and semi-empirical models are constructed. Large-scale simulations are conducted and uncertainty/sensitivity analyses are performed on key parameters. We develop linear algebra software including eigensolvers or parallel architectures in support of high-performance computing. Computer models estimate the performance of real-world phenomenon. The input parameters of the model can be simulated from probability distributions to measure the sensitivities within the model and the uncertainties of the results.

Advanced Computing, Mathematics, and Data


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