A Preliminary Examination of the Application of Unscented Transformation Technique to Error Propagation in Nonlinear Cases of Nuclear Data Science

Kadvekar, Harshavardhan; Khan, Sanaullah; Ram, Sangeetha Prasanna; Nair, Jayakrishnan; Ganesan, S.

doi:10.13182/nse15-103

Cited by 7 publications

(1 citation statement)

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“…The example illustrates how driving mode analysis serves to elucidate that the single‐number summary of propagated output uncertainty is the result of certain correlated functional features either reinforcing with or compensating for one another. Driving mode analysis deconstructs the well‐known sandwich rule [11, 12] into interpretable components that unpack how uncertainties propagate from inputs to outputs. Section 5 summarizes the properties of the driving mode and reiterates the value of displaying and interpreting the sensitivity profile of a computation along with its driving mode with respect to specified input uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Driving mode analysis—How uncertain functional inputs propagate to an output

Vander Wiel,

Grosskopf,

Michaud

et al. 2023

Statistical Analysis

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Driving mode analysis elucidates how correlated features of uncertain functional inputs jointly propagate to produce uncertainty in the output of a computation. Uncertain input functions are decomposed into three terms: the mean functions, a zero‐mean driving mode, and zero‐mean residual. The random driving mode varies along a single direction, having fixed functional shape and random scale. It is uncorrelated with the residual, and under linear error propagation, it produces an output variance equal to that of the full input uncertainty. Finally, the driving mode best represents how input uncertainties propagate to the output because it minimizes expected squared Mahalanobis distance amongst competitors. These characteristics recommend interpretation of the driving mode as the single‐degree‐of‐freedom component of input uncertainty that drives output uncertainty. We derive the functional driving mode, show its superiority to other seemingly sensible definitions, and demonstrate the utility of driving mode analysis in an application. The application is the simulation of neutron transport in criticality experiments. The uncertain input functions are nuclear data that describe how Pu reacts to bombardment by neutrons. Visualization of the driving mode helps scientists understand what aspects of correlated functional uncertainty have effects that either reinforce or cancel one another in propagating to the output of the simulation.

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Section: Introductionmentioning

confidence: 99%