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Fast uncertainty quantification of spent nuclear fuel with neural networks
Albà, A., Adelmann, A., Münster, L., Rochman, D., & Boiger, R. (2024). Fast uncertainty quantification of spent nuclear fuel with neural networks. Annals of Nuclear Energy, 196, 110204 (8 pp.). https://doi.org/10.1016/j.anucene.2023.110204
Probabilistic risk assessment for the piping of a nuclear power plant: uncertainty and sensitivity analysis by using SINTAP procedure
Mao, G., Niffenegger, M., & Mao, X. (2022). Probabilistic risk assessment for the piping of a nuclear power plant: uncertainty and sensitivity analysis by using SINTAP procedure. International Journal of Pressure Vessels and Piping, 200, 104791 (10 pp.). https://doi.org/10.1016/j.ijpvp.2022.104791
Physics-based 0D-U-I-SoC cell performance model for aqueous organic redox flow batteries
Mourouga, G., Schaerer, R. P., Yang, X., Janoschka, T., Schmidt, T. J., & Schumacher, J. O. (2022). Physics-based 0D-U-I-SoC cell performance model for aqueous organic redox flow batteries. Electrochimica Acta, 415, 140185 (18 pp.). https://doi.org/10.1016/j.electacta.2022.140185
Impact of various source of covariance information on integral parameters uncertainty during depletion calculations with CASMO-5
Hursin, M., Rochman, D., Vasiliev, A., Ferroukhi, H., & Pautz, A. (2021). Impact of various source of covariance information on integral parameters uncertainty during depletion calculations with CASMO-5. In M. Margulis & P. Blaise (Eds.), EPJ web of conferences: Vol. 247. PHYSOR2020 - international conference on physics of reactors: transition to a scalable nuclear future (p. 09005 (10 pp.). https://doi.org/10.1051/epjconf/202124709005
A framework based on statistical analysis and stakeholders' preferences to inform weighting in composite indicators
Lindén, D., Cinelli, M., Spada, M., Becker, W., Gasser, P., & Burgherr, P. (2021). A framework based on statistical analysis and stakeholders' preferences to inform weighting in composite indicators. Environmental Modelling and Software, 145, 105208 (16 pp.). https://doi.org/10.1016/j.envsoft.2021.105208
On nonintrusive uncertainty quantification and surrogate model construction in particle accelerator modeling
Adelmann, A. (2019). On nonintrusive uncertainty quantification and surrogate model construction in particle accelerator modeling. SIAM-ASA Journal on Uncertainty Quantification, 7(2), 383-416. https://doi.org/10.1137/16M1061928