论文标题
完全电离等离子体流体模型封闭的神经网络可表示
Neural network representability of fully ionized plasma fluid model closures
论文作者
论文摘要
对于旨在准确描述其感兴趣系统的建模者来说，流体建模中的封闭问题是一个众所周知的挑战。多年来，已经提出了广泛的分析配方，但是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捉摸的目标。在这项研究中，作为构建基于数据的新方法的第一步，我们采用了不断培养的机器学习方法来评估神经网络体系结构的能力，以繁殖流行的磁化等离子体封闭中固有的重要物理学。我们发现令人鼓舞的结果，表明神经网络在关闭物理学上的适用性，但也提出了有关如何为给定的位置属性选择适当的网络体系结构的建议，这些属性由等离子体的物理学决定。
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.
