EPJ Web Conf.
Volume 247, 2021PHYSOR2020 – International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future
|Number of page(s)||8|
|Section||Sensitivity & Uncertainty Methods|
|Published online||22 February 2021|
- M. Abdo, R. Elzohery, and J. A. Roberts. “Modeling isotopic evolution with surrogates based on dynamic mode decomposition.” Annals of Nuclear Energy, volume 129, pp. 280–288 (2019). [Google Scholar]
- R. Elzohery, M. Abdo, and J. Roberts. “Comparison Between Gaussian Processes and DMD Surrogates for Isotopic Composition Prediction.” Transactions of the American Nuclear Society, volume 118, pp. 459–462 (2018). [Google Scholar]
- X. Wu, C. Wang, and T. Kozlowski. “Kriging-based Surrogate Models for Uncertainty Quantification and Sensitivity Analysis.” (2017). [Google Scholar]
- Z. K. Hardy, J. E. Morel, and C. Ahrens. “Dynamic Mode Decomposition for Subcritical Metal Systems.” Nuclear Science and Engineering, pp. 1–13 (2019). [Google Scholar]
- S. Lorenzi. “An Adjoint Proper Orthogonal Decomposition method for a neutronics reduced order model.” Annals of Nuclear Energy, volume 114, pp. 245–258 (2018). [Google Scholar]
- S. Lorenzi, A. Cammi, L. Luzzi, and G. Rozza. “A reduced order model for investigating the dynamics of the Gen-IV LFR coolant pool.” Applied Mathematical Modelling, volume 46, pp. 263–284 (2017). [Google Scholar]
- A. Quarteroni, G. Rozza, and A. Manzoni. “Certified reduced basis approximation for parametrized partial differential equations and applications.” Journal of Mathematics in Industry, volume 1(1), p. 3 (2011). [Google Scholar]
- P. Chen, A. Quarteroni, and G. Rozza. “Reduced order methods for uncertainty quantification problems.” ETH Zurich, SAM Report, volume 3 (2015). [Google Scholar]
- P. Benner, A. Cohen, M. Ohlberger, and K. Willcox. Model reduction and approximation: theory and algorithms, volume 15. SIAM (2017). [Google Scholar]
Initial download of the metrics may take a while.