The linear micro-instabilities driving turbulent transport in magnetized fusion plasmas (as well as the respective nonlinear saturation mechanisms) are known to be sensitive with respect to various physical parameters characterizing the background plasma and the magnetic equilibrium. Therefore, uncertainty quantification is essential for achieving predictive numerical simulations of plasma turbulence. However, the high computational costs of the required gyrokinetic simulations and the large number of parameters render standard Monte Carlo techniques intractable. To address this problem, we propose a multi-fidelity Monte Carlo approach in which we employ data-driven low-fidelity models that exploit the structure of the underlying problem such as low intrinsic dimension and anisotropic coupling of the stochastic inputs. The low-fidelity models are efficiently constructed via sensitivity-driven dimension-adaptive sparse grid interpolation using both the full set of uncertain inputs and subsets comprising only selected, important parameters. We illustrate the power of this method by applying it to two plasma turbulence problems with up to $14$ stochastic parameters, demonstrating that it is up to four orders of magnitude more efficient than standard Monte Carlo methods measured in single-core performance, which translates into a runtime reduction from around eight days to one hour on 240 cores on parallel machines.
Hierarchical Multi-Fidelity Learning for Predicting Three-Dimensional Flame Wrinkling and Turbulent Burning Velocity
Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA（机械与工业工程系，东北大学，波士顿，马萨诸塞州，02115，美国）；School of Mechanical Engineering, University of Leeds, Leeds, UK（机械工程学院，利兹大学，利兹，英国）
Space-dependent turbulence model aggregation using machine learning
在这篇文章中，我们提出了一个数据驱动的方法，结合一组竞争的湍流模型的解决方案。各个模型的预测线性组合，以提供一个合奏的解决方案，伴随着由于湍流模型的选择预测的不确定性的估计。首先，对于一组训练流配置，我们通过引入高保真数据和单个模型预测之间的距离度量，在组件模型表现最好的区域为组件模型分配高权重，反之亦然。然后将模型权重映射到代表局部流物理的特征空间中，并通过随机森林（RF）算法进行回归。RF回归最后被用来推断空间分布的模型权重看不见的配置。新案例的预测被构造为基础模型解的凸线性组合，而模型间方差提供了关于高模型不确定性区域的信息。本文以NACA 65 V103压气机叶栅中Re~ 3e 5的一类流动为例，验证了本文方法的有效性。结果表明，聚合解决方案优于用于通知RF回归量的单个模型的准确性，并且对于与前一个相关性良好的其他量表现良好。估计的不确定性区间通常与目标高保真数据一致。本方法代表了一种可行的方法，用于更客观地选择和组合工程实践中感兴趣的配置中的备选湍流模型
Alignment-Induced Self-Organization of Autonomously Steering Microswimmers: Turbulence, Vortices, and Jets
Forschungszentrum Jülich（于利希研究中心）；Peter Grünberg Institute（彼得·格林贝格研究所）；Jülich Centre for Neutron Science（于利希中子科学中心）；Institute for Advanced Simulation（高级模拟研究所）
