Machine Learning approach to modeling of neutral particles transport in plasma
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915765749284864 |
|---|---|
| author | Umansky, M. V. Parker, G. J. Smirnov, R. D. |
| author_facet | Umansky, M. V. Parker, G. J. Smirnov, R. D. |
| contents | A propagator-based approach is investigated for Monte-Carlo (MC) modeling of neutral particles transport in fusion boundary plasmas. The propagator is essentially a Green function for the neutral kinetic equation, which depends on the plasma profiles. A Neural Network (NN) based model for the propagator provides a fast and accurate solution for the neutral distribution function in plasma. Furthermore, continuous and smooth dependence of NN-based reconstruction of the propagator on the plasma parameters opens the possibility for using this approach with Jacobian-based methods for time-integration and root finding. Initial results from a small 1D test problem look promising; however, important research questions are concerned with the scaling of the algorithm to larger systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23088 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning approach to modeling of neutral particles transport in plasma Umansky, M. V. Parker, G. J. Smirnov, R. D. Plasma Physics A propagator-based approach is investigated for Monte-Carlo (MC) modeling of neutral particles transport in fusion boundary plasmas. The propagator is essentially a Green function for the neutral kinetic equation, which depends on the plasma profiles. A Neural Network (NN) based model for the propagator provides a fast and accurate solution for the neutral distribution function in plasma. Furthermore, continuous and smooth dependence of NN-based reconstruction of the propagator on the plasma parameters opens the possibility for using this approach with Jacobian-based methods for time-integration and root finding. Initial results from a small 1D test problem look promising; however, important research questions are concerned with the scaling of the algorithm to larger systems. |
| title | Machine Learning approach to modeling of neutral particles transport in plasma |
| topic | Plasma Physics |
| url | https://arxiv.org/abs/2510.23088 |