Open Access
Issue |
EPJ Web Conf.
Volume 247, 2021
PHYSOR2020 – International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future
|
|
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Article Number | 20005 | |
Number of page(s) | 9 | |
Section | Global Nuclear Innovation (Special session) | |
DOI | https://doi.org/10.1051/epjconf/202124720005 | |
Published online | 22 February 2021 |
- D.G. Cacuci, The Second-Order Adjoint Sensitivity Analysis Methodology, CRC Press, Taylor & Francis Group, Boca Raton, FL, USA (2018) [Google Scholar]
- D.G. Cacuci, BERRU Predictive Modeling: Best Estimate Results with Reduced Uncertainties. Springer Heidelberg/New York (2018). [Google Scholar]
- Assessment of Existing Nuclear Data Adjustment Methodologies, International Evaluation Co- operation, Intermediate Report of WPEC Subgroup 33, NEA/NSC/WPEC/DOC(2010)429, OECD/NEA, Paris (2011). [Google Scholar]
- M. Goldstein and D. Woof, Bayes Linear Statistics: Theory and Methods. John Wiley & Sons, Chichester, England (2007). [Google Scholar]
- W. Lahoz, B. Khattatov, and R. Ménard, Data Assimilation: Making Sense of Observations, Springer Verlag, New York (2010). [Google Scholar]
- J. M. Lewis, S. Lakshmivarahan, and S.K. Dhall, Dynamic Data Assimilation: A Least Square Approach, Cambridge University Press, Cambridge (2006). [Google Scholar]
- D.G. Cacuci, “Towards Overcoming the Curse of Dimensionality: The Third-Order Adjoint Method for Sensitivity Analysis of Response-Coupled Linear Forward/Adjoint Systems, with Applications to Uncertainty Quantification and Predictive Modeling,” Energies, accepted, November (2019). [Google Scholar]
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