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

Journal Article · · Statistical Analysis and Data Mining
DOI: https://doi.org/10.1002/sam.11646 · OSTI ID:2007967
ORCiD logo [1];  [1];  [1];  [2]
  1. Statistical Sciences Group Los Alamos National Laboratory Los Alamos New Mexico USA
  2. Materials and Physical Data Group Los Alamos National Laboratory Los Alamos New Mexico USA

Abstract 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.

Sponsoring Organization:
USDOE
Grant/Contract Number:
89233218CNA000001
OSTI ID:
2007967
Alternate ID(s):
OSTI ID: 2203412
Journal Information:
Statistical Analysis and Data Mining, Journal Name: Statistical Analysis and Data Mining Journal Issue: 1 Vol. 17; ISSN 1932-1864
Publisher:
Wiley Blackwell (John Wiley & Sons)Copyright Statement
Country of Publication:
United States
Language:
English

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