Physics-Informed Machine Learning Approach to Modeling Line Emission from Helium-Containing Plasmas
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908420928438272 |
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| author | Kajita, Shin |
| author_facet | Kajita, Shin |
| contents | The helium I line intensity ratio (LIR) method is used to measure the electron density ($n_e$) and temperature ($T_e$) of fusion-relevant plasmas. Although the collisional-radiative model (CRM) has been used to predict $n_e$ and $T_e$, recent studies have shown that machine learning approaches can provide better measurements if a sufficient dataset for training is available. This study investigates a hybrid neural network approach that combines CRM- and experiment-based models. Although the CRM-based model alone exhibited negative transfer in most cases, the ensemble model modestly improved the prediction accuracy of $T_e$. Notably, in data-limited scenarios, the CRM-based model outperformed the others for $T_e$ prediction, highlighting its potential for applications with constrained diagnostic access. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20117 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-Informed Machine Learning Approach to Modeling Line Emission from Helium-Containing Plasmas Kajita, Shin Plasma Physics The helium I line intensity ratio (LIR) method is used to measure the electron density ($n_e$) and temperature ($T_e$) of fusion-relevant plasmas. Although the collisional-radiative model (CRM) has been used to predict $n_e$ and $T_e$, recent studies have shown that machine learning approaches can provide better measurements if a sufficient dataset for training is available. This study investigates a hybrid neural network approach that combines CRM- and experiment-based models. Although the CRM-based model alone exhibited negative transfer in most cases, the ensemble model modestly improved the prediction accuracy of $T_e$. Notably, in data-limited scenarios, the CRM-based model outperformed the others for $T_e$ prediction, highlighting its potential for applications with constrained diagnostic access. |
| title | Physics-Informed Machine Learning Approach to Modeling Line Emission from Helium-Containing Plasmas |
| topic | Plasma Physics |
| url | https://arxiv.org/abs/2506.20117 |