Open Access
EPJ Web Conf.
Volume 247, 2021
PHYSOR2020 – International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future
Article Number 00002
Number of page(s) 9
Published online 22 February 2021
  1. D.G. Cacuci, The Second-Order Adjoint Sensitivity Analysis Methodology, CRC Press, Taylor & Francis Group, Boca Raton, FL, USA (2018) [Google Scholar]
  2. D.G. Cacuci, BERRU Predictive Modeling: Best Estimate Results with Reduced Uncertainties. Springer Heidelberg/New York (2018). [Google Scholar]
  3. 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]
  4. M. Goldstein and D. Woof, Bayes Linear Statistics: Theory and Methods. John Wiley & Sons, Chichester, England (2007). [Google Scholar]
  5. W. Lahoz, B. Khattatov, and R. Ménard, Data Assimilation: Making Sense of Observations, Springer Verlag, New York (2010). [Google Scholar]
  6. J. M. Lewis, S. Lakshmivarahan, and S.K. Dhall, Dynamic Data Assimilation: A Least Square Approach, Cambridge University Press, Cambridge (2006). [Google Scholar]
  7. 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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