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Performing linear regression with responses calculated using Monte Carlo transport codes

Price, Dean ; Kochunas, Brendan

Nuclear Engineering and Technology, 2022, 54(5), , pp.1902-1908 [Periódico revisado por pares]

Elsevier B.V

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  • Título:
    Performing linear regression with responses calculated using Monte Carlo transport codes
  • Autor: Price, Dean ; Kochunas, Brendan
  • Assuntos: Linear regression ; Monte Carlo ; Sensitivity analysis ; Uncertainty quantification ; 원자력공학
  • É parte de: Nuclear Engineering and Technology, 2022, 54(5), , pp.1902-1908
  • Notas: KISTI1.1003/JNL.JAKO202218661397913
  • Descrição: In many of the complex systems modeled in the field of nuclear engineering, it is often useful to use linear regression-based analyses to analyze relationships between model parameters and responses of interests. In cases where the response of interest is calculated by a simulation which uses Monte Carlo methods, there will be some uncertainty in the responses. Further, the reduction of this uncertainty increases the time necessary to run each calculation. This paper presents some discussion on how the Monte Carlo error in the response of interest influences the error in computed linear regression coefficients. A mathematical justification is given that shows that when performing linear regression in these scenarios, the error in regression coefficients can be largely independent of the Monte Carlo error in each individual calculation. This condition is only true if the total number of calculations are scaled to have a constant total time, or amount of work, for all calculations. An application with a simple pin cell model is used to demonstrate these observations in a practical problem.
  • Editor: Elsevier B.V
  • Idioma: Inglês;Coreano
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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