Upsampling Monte Carlo neutron transport simulation tallies using a convolutional neural network

The physical quantities calculated by nuclear reactor Monte Carlo simulations are typically recorded on a grid of two or three spatial dimensions and one dimension of neutron energy.Because of this,increasing the resolution of the calculated quantities can have a significant impact on the memory and CPU time required to run a simulation.Convolutional neural networks have been shown to accurately upsample coarse-resolution photo-graphic images to resolutions multiple times finer than the originals.Here we show that a convolutional neural network can accurately upsample flux tallies in a Monte Carlo neutron transport simulation by a factor of two along the spatial and energy dimensions.Neutron flux tallies in pressurized water reactor assemblies were calculated using OpenMC at a 64×64 pixel spatial resolution and 8 neutron energy groups for input to the neural network.The network upsamples the low-resolution neutron flux to 128×128 pixel spatial resolution and 16 neutron energy groups.High-resolution neutron flux tallies and their uncertainties were also calculated with OpenMC and compared with the network’s predictions.The upsampled data and the high-resolution tally results agree to within the statistical uncertainty calculated by OpenMC.