Publication Date
2-28-2026
Document Type
Conference Proceeding
Publication Title
Rarefied Gas Dynamics. RGD 2024. Springer Aerospace Technology. Springer, Cham.
Editor
Grabe, M., Oblapenko, G., Torrilhon, M.
DOI
10.1007/978-3-032-00094-1_56
First Page
581
Last Page
590
Abstract
This paper presents a perspective in which Direct Simulation Monte Carlo (DSMC) is viewed not in its traditional role as an algorithm for solving the Boltzmann equation but as a numerical method for statistical mechanics. First, analytical techniques such as the collision virial and Green-Kubo relations, commonly used in molecular dynamics, are used to study the numerical properties of the DSMC algorithm. The stochastic aspect of DSMC, which is often viewed as unwanted numerical noise, is shown to be a useful feature for problems in statistical physics, such as Brownian motion and thermodynamic fluctuations. Finally, it is argued that fundamental results from statistical mechanics can provide guardrails when applying machine learning to DSMC.
Keywords
Direct simulation Monte Carlo, Statistical mechanics, Molecular dynamics, Kinetic theory, Fluctuations
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Physics and Astronomy
Recommended Citation
Alejandro Garcia. "DSMC: A Statistical Mechanics Perspective" Rarefied Gas Dynamics. RGD 2024. Springer Aerospace Technology. Springer, Cham. (2026): 581-590. https://doi.org/10.1007/978-3-032-00094-1_56
