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This paper discusses a neural network-based approach for fast uncertainty quantification of spent nuclear fuel (SNF) characteristics, aiming to enhance computational efficiency compared to traditional physics-based models. The neural network is trained on data from CASMO5 simulations to accurately predict decay heat and nuclide concentrations based on various input parameters, achieving significant reductions in computational costs. The findings demonstrate the potential of using neural networks as surrogat

Author

tingyau7

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EN