Publications

Overview

  • Morgan, Joanna Piper, et al. “Monte Carlo/Dynamic Code (MC/DC): An accelerated Python package for fully transient neutron transport and rapid methods development.” Journal of Open Source Software 9.96 (2024): 6415. https://joss.theoj.org/papers/10.21105/joss.06415

  • Variansyah, Ilham, et al. “Development of MC/DC: a performant, scalable, and portable Python-based Monte Carlo neutron transport code.” In International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering. Niagara Falls, Ontario, Canada (2023). Preprint: https://arxiv.org/abs/2305.07636

Benchmarking, Verification, and Validation

Software Engineering

  • Morgan, Joanna Piper, et al. “Performant and Portable Monte Carlo Neutron Transport via Numba.” Computing in Science & Engineering (2025). https://ieeexplore.ieee.org/abstract/document/10926859/

  • Cuneo, Braxton, and Mike Bailey. “Divergence reduction in Monte Carlo neutron transport with on-GPU asynchronous scheduling.” ACM Transactions on Modeling and Computer Simulation 34.1 (2024): 1-25. https://dl.acm.org/doi/abs/10.1145/3626957

  • Morgan, J. P., et al. “Explorations of Python-Based Automatic Hardware Code Generation for Neutron Transport Applications”. Transactions of The American Nuclear Society, 1, vol. 126, Zenodo, 2022, https://doi.org/10.5281/zenodo.6646813.

Variance/Runtime Reduction Technique

Hybrid Monte Carlo Transport

  • Pasmann, Samuel, et al. “Mitigating Spatial Error in the Iterative Quasi–Monte Carlo (iQMC) Method for Neutron Transport Simulations with Linear Discontinuous Source Tilting and Effective Scattering and Fission Rate Tallies.” Nuclear Science and Engineering 199.sup1 (2025): S381-S396. https://www.tandfonline.com/doi/abs/10.1080/00295639.2024.2332007

  • Novellino, Vincent N., and Dmitriy Y. Anistratov. Analysis of Hybrid MC/Deterministic Methods for Transport Problems Based on Low-Order Equations Discretized by Finite Volume Scheme. Transaction of American Nuclear Society, v. 130, 2024. Preprint: https://doi.org/10.48550/arXiv.2403.05673

  • Whewell, Ben, et al. “Multigroup neutron transport using a collision-based hybrid method.” Nuclear science and engineering 197.7 (2023): 1386-1405. https://www.tandfonline.com/doi/abs/10.1080/00295639.2022.2154119

  • Pasmann, Sam, et al. “A quasi–Monte Carlo method with Krylov linear solvers for multigroup neutron transport simulations.” Nuclear Science and Engineering 197.6 (2023): 1159-1173. https://www.tandfonline.com/doi/abs/10.1080/00295639.2022.2143704

  • Pasmann, Sam, et al. “A quasi–Monte Carlo method with Krylov linear solvers for multigroup neutron transport simulations.” Nuclear Science and Engineering 197.6 (2023): 1159-1173. https://www.tandfonline.com/doi/abs/10.1080/00295639.2022.2143704

  • Pasmann, Samuel, et al. “iQMC: Iterative Quasi-Monte Carlo with Krylov Linear Solvers for k-Eigenvalue Neutron Transport Simulations.” In International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering. Niagara Falls, Ontario, Canada (2023). Preprint: https://arxiv.org/abs/2306.11600

  • Pasmann, Samuel, Ilham Variansyah, and R. G. McClarren. “Convergent transport source iteration calculations with Quasi-Monte Carlo.” Transactions of the American Nuclear Society 124 (2021): 192-195.

Uncertainty Quantification and Sensitivity Analysis

  • Variansyah, Ilham, Ryan G. McClarren, and Todd S. Palmer. “Derivative Source Method for Monte Carlo Transport Calculation of Sensitivities to Material Densities and Dimensions.” In International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering. Denver, Colorado, USA (2025). Preprint: https://arxiv.org/abs/2501.06397

  • Clements, Kayla B., et al. “A variance deconvolution estimator for efficient uncertainty quantification in Monte Carlo radiation transport applications.” Journal of Quantitative Spectroscopy and Radiative Transfer 319 (2024): 108958. https://www.sciencedirect.com/science/article/pii/S0022407324000657

  • Clements, Kayla, et al. “Global Sensitivity Analysis in Monte Carlo Radiation Transport.” In International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering. Niagara Falls, Ontario, Canada (2023). Preprint: https://arxiv.org/abs/2403.06106

  • Clements, Kayla C., G. Geraci, and Aaron J. Olson. “A variance deconvolution approach to sampling uncertainty quantification for Monte Carlo radiation transport solvers.” Computer Science Research Institute Summer Proceedings 2021 (2021): 293-307. https://www.osti.gov/biblio/1855061

Miscellany

  • Lame, Ethan, et al. “Compressed Sensing Methods for Memory Reduction in Monte Carlo Simulations.” arXiv preprint arXiv:2602.07771 (2026). https://doi.org/10.48550/arXiv.2602.07771

  • Cuneo, Braxton S., and Ilham Variansyah. “An Alternative to Stride-Based RNG for Monte Carlo Transport.” In Transactions of The American Nuclear Society, volume 130 (1), pp. 423–426 (2024). Preprint: https://arxiv.org/abs/2403.06362

  • Variansyah, Ilham, and Ryan G. McClarren. “High-fidelity treatment for object movement in time-dependent Monte Carlo transport simulations.” In International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering. Niagara Falls, Ontario, Canada (2023). Preprint: https://doi.org/10.48550/arXiv.2305.07641

  • Variansyah, Ilham, and Ryan G. McClarren. “An effective initial particle sampling technique for Monte Carlo reactor transient simulations.” In International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering. Niagara Falls, Ontario, Canada (2023). Preprint: https://doi.org/10.48550/arXiv.2305.07646