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physics.comp-ph2026
Reliable Uncertainty Estimation for Machine-Learned Multigroup Cross Sections Without Retraining
Joshua Nichols, Andrew Osborne, Valerio Mascolino
Uncertainty quantification in machine learning models is essential for nuclear energy applications. While machine learning models can make both uncertainty and point predictions, t…
physics.comp-ph2026
Uncertainty Quantification for the Fission Matrix Method: A Rigorous Mathematical Framework and Computationally Efficient Alternatives
Valerio Mascolino
The fission matrix (FM) method recasts neutron transport in matrix form, enabling fast, interpolation-based reactor calculations from a pre-computed database of Monte Carlo-derived…