Evolutionary Discovery of Sequence Acceleration Methods for Slab Geometry Neutron Transport
Abstract
We present a genetic programming approach to automatically discover convergence acceleration methods for discrete ordinates solutions of neutron transport problems in slab geometry. Classical acceleration methods such as Aitken's delta-squared and Wynn epsilon assume specific convergence patterns and do not generalize well to the broad set of transport problems encountered in practice. We evolved mathematical formulas specifically tailored to SN convergence characteristics in this work. The discovered accelerator, featuring second differences and cross-product terms, achieved over 75 percent success rate in improving convergence compared to raw sequences - almost double that observed for classical techniques for the problem set considered. This work demonstrates the potential for discovering novel numerical methods in computational physics via genetic programming and attempts to honor Prof. Ganapol's legacy of advancing experimental mathematics applied to neutron transport.
Keywords
Cite
@article{arxiv.2512.24559,
title = {Evolutionary Discovery of Sequence Acceleration Methods for Slab Geometry Neutron Transport},
author = {Japan K. Patel and Barry D. Ganapol and Anthony Magliari and Matthew C. Schmidt and Todd A. Wareing},
journal= {arXiv preprint arXiv:2512.24559},
year = {2026}
}
Comments
This work was presented at a special conference organized to honor Prof. Ganapol of University of Arizona on his retirement. It has been submitted to a follow-up nse publication call