We perform multilevel Bayesian calibration of a model for fission gas diffusivity in UO2 nuclear fuel. Specifically, we use a two-level delayed acceptance method that couples a machine learning surrogate for Xe and U diffusivities with a …
Computational models of complex physical systems often rely on simplifying assumptions which inevitably introduce model error, with consequent predictive errors. Given data on model observables, the estimation of parameterized model-error …
Abstract This study develops a novel general framework to project the permafrost fate with rigorous uncertainty quantification to assess dominant sources. Borehole temperature records from three sites in the Russian western Arctic are used to …
Mechanistic models informed by lower-length-scale simulations have a role to play in accelerating fuel qualification by enabling the use of separate effects tests to reduce uncertainty on model parameters that impact the predictions of in-reactor …
Abstract Accurate simulation of the quasi-biennial oscillation (QBO) is challenging due to uncertainties in representing convectively generated gravity waves. We develop an end-to-end uncertainty quantification workflow that calibrates these gravity …