The simulation of plasma physics is computationally expensive because the underlying physical system is of high dimensions, requiring three spatial dimensions and three velocity dimensions. One popular numerical approach is Particle-In-Cell (PIC) methods owing to its ease of implementation and favorable scalability in high-dimensional problems. An unfortunate drawback of the method is the introduction of statistical noise resulting from the use of finitely many particles. In this paper we examine the application of the Smoothness-Increasing Accuracy-Conserving (SIAC) family of convolution kernel filters as denoisers for moment data arising from PIC simulations. We show that SIAC filtering is a promising tool to denoise PIC data in the physical space as well as capture the appropriate scales in the Fourier space. Furthermore, we demonstrate how the application of the SIAC technique reduces the amount of information necessary in the computation of quantities of interest in plasma physics such as the Bohm speed.

翻译：等离子体物理的模拟计算成本高昂，因为其底层物理系统具有高维特性，需涵盖三维空间与三维速度维度。粒子网格法因其易于实现且在高维问题中具有良好的可扩展性，成为主流的数值方法之一。然而，该方法存在一个固有缺陷：由于仅使用有限数量的粒子，会引入统计噪声。本文研究了光滑递增精度保持（SIAC）卷积核滤波器族在粒子网格模拟矩数据去噪中的应用。结果表明，SIAC滤波不仅能有效去除物理空间中的粒子网格数据噪声，还能在傅里叶空间中精准捕获相应尺度。此外，我们进一步展示了SIAC技术如何减少等离子体物理中关键物理量（如玻姆速度）计算所需的信息量。