用神经微分方程分析托卡马克等离子体中输运与辐射的敏感性。
Sensitivity Analysis of Transport and Radiation in NeuralPlasmaODE for ITER Burning Plasmas
- 基于神经微分方程构建多区域多时标模型，量化参数敏感性。
- 磁場強度、安全性因子和雜質含量對能量約束影響最顯著。
- 適用於ITER燃燒等離子體預測與優化，助力核聚變運行設計。
理解關鍵物理參數如何影響燃燒等離子體行為，對可靠運行ITER至關重要。本文將基於神經常微分方程的多區域、多時標模型NeuralPlasmaODE擴展，用於分析ITER等離子體中輸運與輻射機制的敏感性。針對感應情景下訓練好的基線模型，計算核心與邊緣溫度、密度對輸運擴散係數、電子回旋輻射（ECR）參數、雜質比例及離子軌道損失（IOL）時間尺度的歸一化敏感度。結果表明，磁場強度、安全性因子和雜質含量是能量約束的主要影響因素，同時揭示了溫度依賴性輸運對自調節行為的貢獻。這些發現驗證了NeuralPlasmaODE在燃燒等離子體環境中進行預測建模與方案優化的實用性。
原文摘要 · Abstract (English)
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.
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