Abstract The results of transport modeling codes, e.g., GENE for the plasma core or SOLPS-ITER for the plasma edge, depend critically on reliable profile and equilibrium estimates. The propagation of uncertainties (UP) of input quantities to the results of modeling codes, e.g., power and particle exhaust and plasma stability, is frequently neglected because of the costs of running the codes as well as because of the missing uncertainty quantification of input quantities. The situation becomes even more cumbersome if profile gradients and their uncertainties are of major concern for transport analyses. Two different techniques are presented to estimate profiles, profile gradients, their uncertainties, and candidate profiles for UP in modeling codes. Markov Chain Monte Carlo sampling of the posterior probability density of an integrated data analysis approach is applied to estimate electron density and temperature profiles. Nonstationary Gaussian process regression is applied to estimate ion temperature and angular velocity profiles. Both methods provide in a natural way profile gradients, profile logarithmic gradients, and their uncertainties. Modeling codes benefit also from reliable equilibrium reconstructions and quantification of the uncertainty of various equilibrium parameters. For the analysis of diagnostics data, the position and uncertainty of flux surfaces as well as of the magnetic axis are important. For plasma transport and stability codes, the estimation of uncertainties of current and q-profiles is presented. For plasma edge codes the position of the separatrix contour and its uncertainty at various poloidal positions is of primary interest especially if steep profile gradients are present. Examples of uncertainties and their sources in magnetic scalar quantities, profiles, and separatrix contours are shown.

中文翻译：

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融合建模代码的轮廓和平衡的估计和不确定性

摘要 传输建模代码的结果，例如，用于等离子体核心的 GENE 或用于等离子体边缘的 SOLPS-ITER，关键取决于可靠的剖面和平衡估计。由于运行代码的成本以及缺少输入量的不确定性量化，输入量的不确定性 (UP) 对建模代码结果的传播，例如功率和粒子排放以及等离子体稳定性，经常被忽略. 如果剖面梯度及其不确定性是传输分析的主要关注点，情况会变得更加麻烦。介绍了两种不同的技术来估计剖面、剖面梯度、它们的不确定性以及建模代码中 UP 的候选剖面。集成数据分析方法的后验概率密度的马尔可夫链蒙特卡罗采样用于估计电子密度和温度分布。非平稳高斯过程回归用于估计离子温度和角速度分布。这两种方法都以自然的方式提供剖面梯度、剖面对数梯度及其不确定性。建模代码也受益于可靠的平衡重建和各种平衡参数的不确定性的量化。对于诊断数据的分析，磁通表面以及磁轴的位置和不确定性很重要。对于等离子体传输和稳定性代码，介绍了电流和 q 曲线不确定性的估计。对于等离子边缘编码，分界线轮廓的位置及其在各种极向位置的不确定性是主要关注点，尤其是在存在陡峭轮廓梯度的情况下。显示了磁标量、剖面和分界线轮廓中的不确定性及其来源的示例。