The closure problem in fluid modeling is a well-known challenge to modelers aiming to accurately describe their system of interest. Over many years, analytic formulations in a wide range of regimes have been presented but a practical, generalized fluid closure for magnetized plasmas remains an elusive goal. In this study, as a first step towards constructing a novel data based approach to this problem, we apply ever-maturing machine learning methods to assess the capability of neural network architectures to reproduce crucial physics inherent in popular magnetized plasma closures. We find encouraging results, indicating the applicability of neural networks to closure physics but also arrive at recommendations on how one should choose appropriate network architectures for given locality properties dictated by underlying physics of the plasma.

中文翻译：

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完全电离等离子体流体模型闭包的神经网络可表示性

流体建模中的闭合问题是建模人员的一个众所周知的挑战，旨在准确描述他们感兴趣的系统。多年来，已经提出了广泛范围的分析公式，但用于磁化等离子体的实用、广义的流体闭合仍然是一个难以实现的目标。在这项研究中，作为构建基于数据的新方法解决该问题的第一步，我们应用不断成熟的机器学习方法来评估神经网络架构再现流行磁化等离子体闭合中固有的关键物理的能力。我们发现了令人鼓舞的结果，表明神经网络对闭合物理学的适用性，但也提出了关于如何为等离子体的基础物理学所规定的给定局部属性选择合适的网络架构的建议。