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DOE OSTI · 3001916

Uncertainty Quantification for Neutron Shield Using Convolutional Neural Networks

Zamora, J. C. [Michigan State University, East Lansing, MI (United States)] (ORCID:0000000241884354)·Bollen, G. [Michigan State University, East Lansing, MI (United States)]·Ginter, T. [Michigan State University, East Lansing, MI (United States)]·Chowdhury, R. Pal [Michigan State University, East Lansing, MI (United States)]
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Abstract

Uncertainty quantification from radiation transport calculations was conducted using a Bayesian inference approach. A surrogate model, using a convolutional neural network, was employed to emulate the neutron fluence, which was simulated with a Monte Carlo radiation transport model. This allowed for a computationally cheap approach to evaluate input parameters and to sample their corresponding posterior probability distributions. Experimental data from the literature were employed to perform uncertainty quantification studies for concrete shields. As a result, the method is a nonintrusive approach that enables studies with multiple input parameters and can be applied to any radiation transport model.

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Bayesian inferenceConvolutional neural networkneutron shieldradiation transportuncertainty quantification

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Zamora, J. C. [Michigan State University, East Lansing, MI (United States)] (ORCID:0000000241884354), Bollen, G. [Michigan State University, East Lansing, MI (United States)], Ginter, T. [Michigan State University, East Lansing, MI (United States)], Chowdhury, R. Pal [Michigan State University, East Lansing, MI (United States)]. 2025-08-14. Uncertainty Quantification for Neutron Shield Using Convolutional Neural Networks. https://doi.org/10.1080/00295450.2025.2521881

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