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An unsupervised machine-learning checkpoint-restart algorithm using Gaussian mixtures for particle-in-cell simulations
Journal of Computational Physics ( IF 4.1 ) Pub Date : 2021-03-16 , DOI: 10.1016/j.jcp.2021.110185
G. Chen , L. Chacón , T.B. Nguyen

We propose an unsupervised machine-learning checkpoint-restart (CR) algorithm for particle-in-cell (PIC) algorithms using Gaussian mixtures (GM). The algorithm compresses the particle population per spatial cell by constructing a velocity distribution function using GM. Particles are reconstructed at restart time by local resampling of the Gaussians. To guarantee fidelity of the CR process, we ensure the exact preservation of invariants such as charge, momentum, and energy for both compression and reconstruction stages, everywhere on the mesh. We also ensure the preservation of Gauss' law after particle reconstruction by exactly matching the density profile at restart time. As a result, the GM CR algorithm is shown to provide a clean, conservative restart capability while potentially affording orders of magnitude savings in input/output requirements. We demonstrate the algorithm using a recently developed exactly energy- and charge-conserving PIC algorithm using both electrostatic and electromagnetic tests. The tests demonstrate not only a high-fidelity CR capability, but also its potential for enhancing the fidelity of the PIC solution for a given particle resolution.



中文翻译:

使用高斯混合物的无监督机器学习检查点重启算法,用于粒子内仿真

我们为使用高斯混合(GM)的单元格粒子(PIC)算法提出了一种无监督的机器学习检查点重启(CR)算法。该算法通过使用GM构造速度分布函数来压缩每个空间单元的粒子数量。在重新启动时,通过对高斯进行局部重采样来重建粒子。为了保证CR过程的逼真度,我们确保在网格上的任何地方都精确保留不变的变量,例如压缩和重构阶段的电荷,动量和能量。通过在重新启动时精确匹配密度分布图,我们还确保了粒子重建后的高斯定律得以保留。结果,显示了GM CR算法可提供干净,保守的重启功能,同时有可能在输入/输出要求方面节省几个数量级。我们使用静电和电磁测试,使用最近开发的精确节能和节省电荷的PIC算法演示该算法。这些测试不仅证明了高保真CR性能,而且还展示了在给定的颗粒分辨率下增强PIC解决方案保真度的潜力。

更新日期:2021-03-22
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