A novel direct numerical method to calculate the electron velocity distribution function (EVDF) in hydrodynamic equilibrium under a uniform DC electric field is presented. In the present method, an artificial feedforward neural network learns the EVDF governed by both the Boltzmann equation and boundary conditions. The present method dost not require the expansion of the EVDF in the Legendre polynomials and the discretization of both the EVDF and the Boltzmann equation. As a benchmark, the EVDF in Reid’s ramp model gas and Ar gas was calculated by the present method, and then the validity of the present method was demonstrated by comparing electron energy distributions and electron transport coefficients deduced from the EVDF with those calculated by Monte Carlo simulation.

中文翻译：

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深度学习解决弱电离等离子体中电子的玻尔兹曼方程

提出了一种新的直接数值方法，用于计算均匀直流电场作用下流体动平衡中的电子速度分布函数（EVDF）。在本方法中，人工前馈神经网络学习由Boltzmann方程和边界条件控制的EVDF。本方法不需要在勒让德多项式中扩展EVDF，也不需要EVDF和Boltzmann方程的离散化。作为基准，通过本方法计算了里德斜波模型气体和氩气中的EVDF，然后通过将由EVDF推导的电子能量分布和电子传输系数与由Monte Carlo计算得到的电子能量分布和电子传输系数进行比较，证明了本方法的有效性。模拟。