Camille Palmer | Searcharxiv

arXiv2026

Compressed Sensing Methods for Memory Reduction in Monte Carlo Simulations

Monte Carlo simulations of neutronic systems are computationally intensive and demand significant memory resources for high-fidelity modeling. Compressed sensing enables accurate reconstruction of signals from significantly fewer samples than traditional methods. The specific implementation of compressed sensing investigated here involves the use of overlapping cells to collect tallies. Increasing the number of samples improves the reconstruction accuracy, although the marginal gains diminish with more samples. Reconstruction quality is strongly influenced by the sparsity parameter used in basis pursuit denoising. Across the three test cases considered, memory reductions of up to 81.25% (96.25%) are demonstrated for 2D (3D) reconstructions, with select scenarios achieving reconstruction errors within 1 standard deviation of the corresponding high-fidelity reference results.

physics.comp-ph↗

arXiv2025

Interplay of Variance Reduction and Population Control in Monte Carlo Neutron Transport

Monte Carlo methods are widely used for neutron transport simulations at least partly because of the accuracy they bring to the modeling of these problems. However, the computational burden associated with the slow convergence rate of Monte Carlo poses a significant challenge to running large-scale simulations. The continued improvement in high-performance computing capabilities has put exascale time-dependent Monte Carlo neutron transport simulations within reach. Variance reduction techniques have become an essential component to the efficiency of steady-state simulations, and population control techniques are an integral part of time-dependent simulations, but combining them can create algorithmic conflicts. This study investigates the performance of steady-state variance reduction techniques when extended to time-dependent problems and examines how variance reduction and population control techniques combine to impact the effectiveness of time-dependent simulations. Simulations were conducted using various combinations of these techniques across multiple test problems to assess their performance. While this study does not examine all possible variance reduction and population control combinations, the findings emphasize the importance of carefully selecting algorithms to simulate large-scale time-dependent problems effectively. Notably, using weight windows with weight-based combing for population control can significantly hinder simulation performance, whereas pairing weight windows with uniform combing can provide the efficiencies necessary for successfully computing the results of massive problems. Further performance gains were observed when steady-state weight windows were replaced with time-dependent versions.

physics.comp-ph↗