托卡马克中离子损失锥边界的贝叶斯主动学习
Bayesian Active Learning of Ion Loss-Cone Boundaries in Tokamaks
- Oak Ridge National Laboratory（橡树岭国家实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文将托卡马克损失锥边界确定建模为主动学习问题，用贝叶斯逻辑回归与轨道积分器生成损失概率数据库，并展示其在损失分数计算、边缘旋转预测及神经网络代理中的应用。
AI中文摘要：
托卡马克中的损失锥边界确定被构建为一个主动学习问题，其中具有径向基函数特征的贝叶斯逻辑回归模型充当学习器，引导中心轨道积分器充当标注方法。该方法应用于解析托卡马克平衡，跨越发射位置网格，生成具有近似后验不确定性的损失概率模型数据库，每个模型在规定的轨迹预算内生成。一旦生成，该数据库支持下游应用，本文以三个示例加以说明。首先，将概率模型对麦克斯韦分布积分，得到损失离子分数以及损失离子携带的能量和平行动量，后验不确定性解析地传播到每个积分。其次，由此产生的轨道损失力矩与径向角动量扩散之间的稳态平衡给出同向边缘旋转，其幅度主要由规定的动量扩散率和损失区域更新率决定。第三，在数据库上训练的神经网络代理学习从发射位置到损失概率图像的区域映射，其预测计算的输运积分在来自同一网格的保留样本上密切再现贝叶斯参考值。
英文摘要：
Loss-cone boundary determination in tokamaks is cast as an active learning problem in which a Bayesian logistic regression model with radial basis function features acts as the learner and the guiding-center orbit integrator acts as the labeling method. The approach is applied to an analytic tokamak equilibrium across a mesh of launch positions, producing a database of loss-probability models with approximate posterior uncertainty, each generated within a prescribed trajectory budget. Once generated, the database supports downstream applications, illustrated here with three examples. First, integrating the probability models against a Maxwellian distribution yields the lost-ion fraction and the energy and parallel momentum carried by lost ions, with posterior uncertainty propagated analytically to each integral. Second, a steady balance between the resulting orbit-loss torque and radial angular-momentum diffusion gives co-current edge rotation whose amplitude is set mainly by the prescribed momentum diffusivity and loss-region renewal rate. Third, a neural network surrogate trained on the database learns the in-domain mapping from launch position to loss-probability image, and transport integrals computed from its predictions closely reproduce the Bayesian reference values on held-out samples from the same mesh.
