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12 April 2021 Uncertainty contribution estimation for Monte Carlo uncertainty quantification via subspace analysis: neutronics case study
Bassam A. Khuwaileh, Mohammad A. AlShabi, Mamdouh El Haj Assad
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Proceedings Volume 11730, Big Data III: Learning, Analytics, and Applications; 117300C (2021) https://doi.org/10.1117/12.2587273
Event: SPIE Defense + Commercial Sensing, 2021, Online Only
Abstract
This work introduces a monte carlo based technique powered with the ability to estimate the individual uncertainty contributions of each model parameter. The proposed technique utilizes the so-called parameter space analysis to identify the importance of influential Degrees of Freedom (DoFs) with respect to the uncertainty quantification problem. Once determined, these DoFs can be used to define and solve a linear system of equations based on linearizing the model of interest to determine the uncertainty contribution of each DoFs in conjunction with the monte carlo based samples.
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Bassam A. Khuwaileh, Mohammad A. AlShabi, and Mamdouh El Haj Assad "Uncertainty contribution estimation for Monte Carlo uncertainty quantification via subspace analysis: neutronics case study", Proc. SPIE 11730, Big Data III: Learning, Analytics, and Applications, 117300C (12 April 2021); https://doi.org/10.1117/12.2587273
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KEYWORDS
Monte Carlo methods
Statistical modeling
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