Data-driven construction of a generalized kinetic collision operator from molecular dynamics
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866909566402297856 |
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| author | Zhao, Yue Burby, Joshua W. Christlieb, Andrew Lei, Huan |
| author_facet | Zhao, Yue Burby, Joshua W. Christlieb, Andrew Lei, Huan |
| contents | We introduce a data-driven approach to learn a generalized kinetic collision operator directly from molecular dynamics. Unlike the conventional (e.g., Landau) models, the present operator takes an anisotropic form that accounts for a second energy transfer arising from the collective interactions between the pair of collision particles and the environment. Numerical results show that preserving the broadly overlooked anisotropic nature of the collision energy transfer is crucial for predicting the plasma kinetics with non-negligible correlations, where the Landau model shows limitations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_24208 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-driven construction of a generalized kinetic collision operator from molecular dynamics Zhao, Yue Burby, Joshua W. Christlieb, Andrew Lei, Huan Computational Physics Machine Learning Numerical Analysis Plasma Physics We introduce a data-driven approach to learn a generalized kinetic collision operator directly from molecular dynamics. Unlike the conventional (e.g., Landau) models, the present operator takes an anisotropic form that accounts for a second energy transfer arising from the collective interactions between the pair of collision particles and the environment. Numerical results show that preserving the broadly overlooked anisotropic nature of the collision energy transfer is crucial for predicting the plasma kinetics with non-negligible correlations, where the Landau model shows limitations. |
| title | Data-driven construction of a generalized kinetic collision operator from molecular dynamics |
| topic | Computational Physics Machine Learning Numerical Analysis Plasma Physics |
| url | https://arxiv.org/abs/2503.24208 |