Estimation of sensitivity coefficient based on lasso-type penalized linear regression

Katano, Ryota; Endo, Tomohiro*; Yamamoto, Akio*; Tsujimoto, Kazufumi

Journal of Nuclear Science and Technology, 55(10), p.1099 - 1109, 2018/10

https://doi.org/10.1080/00223131.2018.1479988

In this study, we propose the penalized regression "adaptive smooth-lasso" for the estimation of sensitivity coefficients of the neutronics parameters. The proposed method estimates the sensitivity coefficients of the neutronics parameters using the variation of the microscopic cross sections and the neutronics parameter obtained by random sampling. The proposed method utilizes only the forward calculations. Thus, the proposed method can be applied for the complex reactor analysis for which the application of the adjoint method is difficult. In this study, we proposed a penalty term considering the characteristics of the sensitivity coefficients of the neutronics parameter to the microscopic multi-group cross sections. Through verification calculation, we show that the proposed method achieves high accuracy with less computational cost compared to the method based on random sampling proposed in the previous studies.

Prediction of the operating control rod position of the HTTR with supervised machine learning

Ho, H. Q.; Nagasumi, Satoru; Shimazaki, Yosuke; Hamamoto, Shimpei; Iigaki, Kazuhiko; Goto, Minoru; Simanullang, I. L.*; Fujimoto, Nozomu*; Ishitsuka, Etsuo

no journal, ,