Sensitivity Analysis of Transport and Radiation in NeuralPlasmaODE for ITER Burning Plasmas
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909686892068864 |
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| author | Liu, Zefang Stacey, Weston M. |
| author_facet | Liu, Zefang Stacey, Weston M. |
| contents | Understanding how key physical parameters influence burning plasma behavior is critical for the reliable operation of ITER. In this work, we extend NeuralPlasmaODE, a multi-region, multi-timescale model based on neural ordinary differential equations, to perform a sensitivity analysis of transport and radiation mechanisms in ITER plasmas. Normalized sensitivities of core and edge temperatures and densities are computed with respect to transport diffusivities, electron cyclotron radiation (ECR) parameters, impurity fractions, and ion orbit loss (IOL) timescales. The analysis focuses on perturbations around a trained nominal model for the ITER inductive scenario. Results highlight the dominant influence of magnetic field strength, safety factor, and impurity content on energy confinement, while also revealing how temperature-dependent transport contributes to self-regulating behavior. These findings demonstrate the utility of NeuralPlasmaODE for predictive modeling and scenario optimization in burning plasma environments. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_09432 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sensitivity Analysis of Transport and Radiation in NeuralPlasmaODE for ITER Burning Plasmas Liu, Zefang Stacey, Weston M. Plasma Physics Machine Learning Understanding how key physical parameters influence burning plasma behavior is critical for the reliable operation of ITER. In this work, we extend NeuralPlasmaODE, a multi-region, multi-timescale model based on neural ordinary differential equations, to perform a sensitivity analysis of transport and radiation mechanisms in ITER plasmas. Normalized sensitivities of core and edge temperatures and densities are computed with respect to transport diffusivities, electron cyclotron radiation (ECR) parameters, impurity fractions, and ion orbit loss (IOL) timescales. The analysis focuses on perturbations around a trained nominal model for the ITER inductive scenario. Results highlight the dominant influence of magnetic field strength, safety factor, and impurity content on energy confinement, while also revealing how temperature-dependent transport contributes to self-regulating behavior. These findings demonstrate the utility of NeuralPlasmaODE for predictive modeling and scenario optimization in burning plasma environments. |
| title | Sensitivity Analysis of Transport and Radiation in NeuralPlasmaODE for ITER Burning Plasmas |
| topic | Plasma Physics Machine Learning |
| url | https://arxiv.org/abs/2507.09432 |