用于ITER中高能粒子输运预测的机器学习代理模型
Machine-learning surrogate models for nonlinear energetic-particle transport predictions in ITER
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中文总结 AI 辅助
本研究针对ITER稳态工况，开发了高斯过程回归与分层神经网络两种机器学习代理模型，可高精度快速预测高能粒子输运并估计不确定性，计算成本较原模拟降低5至6个数量级，为集成建模提供支撑。
中文摘要 AI 辅助
快速且准确地预测由阿尔芬本征模（AE）不稳定性驱动的高能粒子输运，对用于燃烧等离子体聚变反应堆设计与优化的集成建模工作流至关重要。本研究针对ITER稳态工况，开发了基于机器学习的代理模型，用于快速预测高能束和α粒子的输运通量，同时给出预测不确定性估计。采用两种互补的代理方法：高斯过程（GP）回归和分层神经网络（NN），利用高能粒子输运的非线性FAR3d gyrofluid模拟进行训练。通量变异性分析表明，所选等离子体状态表征为采样特征空间的大部分区域提供了足够独特的非线性输运响应参数化，从而为代理模型的构建提供了依据。两种代理模型均以高预测精度复现了非线性输运通量，同时与直接非线性FAR3d模拟相比，将输运评估的计算成本降低了约5至6个数量级。尽管两种方法达到了相当的预测精度，但表现出不同的不确定性特征：GP提供更一致的全局不确定性估计，而NN能更清晰地区分不同的输运 regime。本研究为开发高能粒子输运的机器学习代理模型建立了概念验证，这些模型具有足够的精度和计算效率，可纳入未来的集成建模工作流。
英文摘要
Fast and accurate prediction of energetic-particle transport driven by Alfvén eigenmode (AE) instabilities is essential for integrated modeling workflows used in the design and optimization of burning plasma fusion reactors. In this work, we develop machine-learning-based surrogate models for rapid prediction of energetic beam and alpha-particle transport fluxes, together with predictive uncertainty estimates, for an ITER steady-state scenario. Two complementary surrogate methodologies, Gaussian process (GP) regression and hierarchical neural networks (NNs), are trained using nonlinear FAR3d gyrofluid simulations of energetic-particle transport. A flux-variability analysis demonstrates that the selected plasma-state representation provides a sufficiently unique parameterization of the nonlinear transport response over most of the sampled feature space, thereby justifying the surrogate formulation. Both surrogate models reproduce the nonlinear transport fluxes with high predictive accuracy while reducing the computational cost of transport evaluation by approximately five to six orders of magnitude relative to direct nonlinear FAR3d simulations. Although the two approaches achieve comparable predictive accuracy, they exhibit distinct uncertainty characteristics: the GP provides more consistent global uncertainty estimates, whereas the NN more clearly distinguishes between different transport regimes. This work establishes a proof of concept for developing machine-learning surrogate models of energetic-particle transport that are sufficiently accurate and computationally efficient to be incorporated into future integrated modeling workflows.
