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 …
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 This study develops a surrogate-based method to assess the uncertainty within a convective permitting integrated modeling system of the Great Lakes region, arising from interacting physics parameterizations across the lake, atmosphere, and …
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 …
Engineering and applied science rely on computational experiments to rigorously study physical systems. The mathematical models used to probe these systems are highly complex, and sampling-intensive studies often require prohibitively many …
Surrogate model development is a critical step for uncertainty quantification or other sample-intensive tasks for complex computational models. In this work we develop a multi-output surrogate form using a class of neural networks (NNs) that employ …