The raw data generated by simulation codes on supercomputers can be so large that it requires data reduction methods to allow scientists to understand it. Physics-based reductions are often used, for example, taking moments of particle distribution functions. It must be realized, however, that there will be a loss of information in these reductions. Here, we explore the use of unsupervised machine learning algorithms to see if patterns and structure can be learned and discovered directly in the data itself, before any reductions, and to give researchers further insights into areas of interest. This has the potential benefit of discovering kinetic structure that would be lost by some physics-based reductions. We utilize the 5-D, gyrokinetic distribution function in simulations from the full-f code X-point Gyrokinetic Code (XGC1). We find that in spatial regions of “blobby” turbulence in the edge, the electron distribution function has a very distinct signature, with higher energy regions varying across space separately from the lower energy component and higher energy regions showing a distinction near passed/trapped boundaries.

中文翻译：

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使用无监督机器学习在粒子分布函数的大数据集中查找结构


超级计算机上的模拟代码生成的原始数据可能非常大，以至于需要数据缩减方法才能让科学家理解它。经常使用基于物理的简化，例如，获取粒子分布函数的矩。然而，必须认识到，这些减少将会造成信息损失。在这里，我们探索使用无监督机器学习算法，看看在进行任何缩减之前是否可以直接在数据本身中学习和发现模式和结构，并让研究人员进一步深入了解感兴趣的领域。这具有发现一些基于物理的还原会丢失的动力学结构的潜在好处。我们在全 f 代码 X 点回旋代码 (XGC1) 的模拟中利用 5 维回旋分布函数。我们发现，在边缘“斑点”湍流的空间区域中，电子分布函数具有非常明显的特征，较高能量区域在空间上与较低能量成分分开变化，而较高能量区域在通过/捕获边界附近表现出区别。