Inference and De-Noising of Non-Gaussian Particle Distribution Functions: A Generative Modeling Approach
Inference and De-Noising of Non-Gaussian Particle Distribution Functions: A Generative Modeling Approach
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DOI：
10.1007/978-3-030-95467-3_25
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发表时间：
2021-10
期刊：
影响因子：
--
通讯作者：
J. Donaghy;K. Germaschewski
中科院分区：
文献类型：
--
作者：
J. Donaghy;K. Germaschewski
等离子体物理的粒子网格数值方法在计算成本和固有噪声之间进行了权衡。对这些模拟产生的数据的推断通常包括对数据进行分箱以恢复粒子分布函数，由此可以研究物理过程。除了包含噪声之外，分布函数是时间动态的，并且可以是非高斯和多模态的，使得建模任务变得困难。在这里，我们演示了使用归一化流来学习一个平滑的，易于处理的近似噪声粒子分布函数。我们证明，由此产生的数据驱动的可能性保存相关的物理，并可以扩展到封装的分布函数的时间演化。
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.