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因底层物理系统具有高维特性（需三维空间与三维速度维度）而计算成本高昂。粒子网格法（PIC）因其易于实现且在高维问题中具有良好的可扩展性，成为常用数值方法之一。然而该方法的一个缺陷是，由于使用有限数量的粒子，会引入统计噪声。本文研究了将提高光滑性保持精度（SIAC）卷积核滤波器族作为去噪工具，应用于PIC模拟中矩数据的处理。研究表明，SIAC滤波既能有效在物理空间中对PIC数据进行去噪，又能捕捉傅里叶空间中的适当尺度。此外，我们展示了SIAC技术的应用如何减少计算等离子体物理中感兴趣量（如玻姆速度）所需的信息量。