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.
POC: Alex Tartakovsky