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

References

• Iwamoto H, Nishihara K, Sugawara T, et al. Sensitivity and uncertainty analysis for an accelerator driven system with JENDL-4.0. J Nucl Sci Technol. 2013;50:856–862.
   Web of Science ®Google Scholar
• Takeda T, Yoshimura Y. Prediction uncertainty evaluation methods to core performance parameters in large liquid-metal fast breeder reactor. Nucl Sci Eng. 1989;103:257.
   Web of Science ®Google Scholar
• Chiba G, Narabayashi T. Variance reduction factor of nuclear data for integral neutronics parameters. Nuclear Data Sheets. 2015;123:62–67.
   Web of Science ®Google Scholar
• Usachev LN. Perturbation theory for the breeding ratio and for other number ratios pertaining to various reactor processes. J Nucl Energy A/B. 1964;18:571–583.
   Web of Science ®Google Scholar
• Williams M. Development of depletion perturbation theory for coupled neutron/nuclide fields. Nucl Sci Eng. 1979;70:20–36.
   Web of Science ®Google Scholar
• Chiba G, Pyeon CH, WFG VR, et al. Nuclear data-induced uncertainty quantification of neutronics parameters of accelerator-driven system. J Nucl Sci Technol. 2016;53:1653–1661.
   Web of Science ®Google Scholar
• Katano R, Endo T, Yamamoto A. Estimation of sensitivity coefficients of core characteristics based on reduced-order modeling using sensitivity matrix of assembly characteristics. J Nucl Sci Thecnol. 2017;54:637–647.
   Web of Science ®Google Scholar
• Kennedy C, Rabiti C, Abdel-Khalik H. Generalized perturbation theory free sensitivity analysis for eigenvalue problems. Nucl Technol. 2012;179:169–179.
   Web of Science ®Google Scholar
• Wang C, Abdel-Khalik H. Exact-to-precision generalized perturbation theory for reactor design calculations. In: Proc PHYSOR 2014; 2014 Sep 28–Oct 3; Kyoto, Japan.
   Google Scholar
• Bang Y, Abdel-Khalik H. Sensitivity analysis via reduced order adjoint method. In: Proc PHYSOR 2014; 2014 Sep 28–Oct 3; Kyoto, Japan.
   Google Scholar
• Abdo M, Abdel-Khalik H. Development of multi-level reduced order modeling methodology. Trans Am Nucl Soc. 2015;112:445–448.
   Google Scholar
• Chiba G, Kawamoto Y, Tsuji M, et al. Estimation of neutronics parameter sensitivity to nuclear data in random sampling-based uncertainty quantification calculations. Ann Nucl Energy. 2015;75:395–403.
   Web of Science ®Google Scholar
• Watanabe T, Endo T, Yamamoto A, et al. Estimation of sensitivity coefficient using random sampling and L1-norm minimization. Trans Am Nucl Soc. 2014;111:1391–1394.
   Google Scholar
• Candes E, Wakin M. An introduction to compressive sampling. IEEE Signal Process Mag. 2008;25(2):21–30.
   Web of Science ®Google Scholar
• Tibshirani R. Regression shrinkage and selection via the lasso. J. R Statist Soc. 1996;58:267–288.
   Google Scholar
• Hawkins D, Maboudou-Tchao E. Smoothed linear modeling for smooth spectral data. Int J Spectrosc. 2013; [8 p.].
   Google Scholar
• Nozad M. Sparse ridge fusion for linear regression [ master’s thesis]. Orlando (FL): the University of Central Florida; 2013.
   Google Scholar
• Lund KV. The instability of cross-validated lasso [ master’s thesis]. Oslo (Norway). University of Oslo; 2013.
   Google Scholar
• Tsujimoto K, Sasa T, Nishihara K, et al. Neutronics design for lead-bismuth cooled accelerator-driven System for transmutation of minor actinide. J Nucl Sci Technol. 2004;41:21–36.
   Web of Science ®Google Scholar
• Sugawara T, Nishihara K, Iwamoto H, et al. Development of three-dimensional reactor analysis code system for accelerator-driven system, ADS3D and its application with subcriticality adjustment mechanism. J Nucl Sci Technol. 2016;53:2018–2027.
   Web of Science ®Google Scholar
• Sugino K, Jin T, Hazama T, et al. Preparation of fast reactor group constant sets UFLIB.J40 and JFS-3-J4.0 based on the JENDL-4.0 data. Ibaraki (Japan): Japan Atomic Energy Agency; 2012. JAEA-Data/Code 2011-017.
   Google Scholar
• Efron B. The jackknife, the bootstrap, and other resampling plans. Philadelphia (PA): Society for Industrial and Applied Mathematics; 1982.
   Google Scholar