The particle-in-cell numerical method of plasma physics balances a trade-off between computational cost and intrinsic noise. Inference on data produced by these simulations generally consists of binning the data to recover the particle distribution function, from which physical processes may be investigated. In addition to containing noise, the distribution function is temporally dynamic and can be non-gaussian and multi-modal, making the task of modeling it difficult. Here we demonstrate the use of normalizing flows to learn a smooth, tractable approximation to the noisy particle distribution function. We demonstrate that the resulting data driven likelihood conserves relevant physics and may be extended to encapsulate the temporal evolution of the distribution function.

翻译：等离子物理的粒子细胞数字方法平衡了计算成本和内在噪音之间的权衡。根据这些模拟产生的数据推论,通常包括将数据捆绑起来以回收粒子分布功能,从中可以对物理过程进行调查。除了含有噪音外,分布功能具有时间动态,并且可以是非古西文和多式的,因此难以进行模型制作。我们在这里演示了使用正常流来学习对噪音粒子分布功能的平稳、可移植近似。我们证明,由此产生的数据驱动的可能性保存了相关的物理学,并可能扩大到包含分布功能的时间演变。