Application of Neural Ordinary Differential Equations for Tokamak Plasma Dynamics Analysis
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916145694507008 |
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| author | Liu, Zefang Stacey, Weston M. |
| author_facet | Liu, Zefang Stacey, Weston M. |
| contents | In the quest for controlled thermonuclear fusion, tokamaks present complex challenges in understanding burning plasma dynamics. This study introduces a multi-region multi-timescale transport model, employing Neural Ordinary Differential Equations (Neural ODEs) to simulate the intricate energy transfer processes within tokamaks. Our methodology leverages Neural ODEs for the numerical derivation of diffusivity parameters from DIII-D tokamak experimental data, enabling the precise modeling of energy interactions between electrons and ions across various regions, including the core, edge, and scrape-off layer. These regions are conceptualized as distinct nodes, capturing the critical timescales of radiation and transport processes essential for efficient tokamak operation. Validation against DIII-D plasmas under various auxiliary heating conditions demonstrates the model's effectiveness, ultimately shedding light on ways to enhance tokamak performance with deep learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_01635 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Application of Neural Ordinary Differential Equations for Tokamak Plasma Dynamics Analysis Liu, Zefang Stacey, Weston M. Plasma Physics Machine Learning In the quest for controlled thermonuclear fusion, tokamaks present complex challenges in understanding burning plasma dynamics. This study introduces a multi-region multi-timescale transport model, employing Neural Ordinary Differential Equations (Neural ODEs) to simulate the intricate energy transfer processes within tokamaks. Our methodology leverages Neural ODEs for the numerical derivation of diffusivity parameters from DIII-D tokamak experimental data, enabling the precise modeling of energy interactions between electrons and ions across various regions, including the core, edge, and scrape-off layer. These regions are conceptualized as distinct nodes, capturing the critical timescales of radiation and transport processes essential for efficient tokamak operation. Validation against DIII-D plasmas under various auxiliary heating conditions demonstrates the model's effectiveness, ultimately shedding light on ways to enhance tokamak performance with deep learning. |
| title | Application of Neural Ordinary Differential Equations for Tokamak Plasma Dynamics Analysis |
| topic | Plasma Physics Machine Learning |
| url | https://arxiv.org/abs/2403.01635 |