论文标题
使用深神经网络从群数据中提取电子散射截面
Extracting Electron Scattering Cross Sections from Swarm Data using Deep Neural Networks
论文作者
论文摘要
电子中性散射横截面是当今许多技术应用的低温等离子体模拟中的基本数量。从这些显微镜横截面中，可以计算几个宏观尺度数量（称为“群”参数）。但是，横截面的测量以及理论计算具有挑战性。自1960年代以来，研究人员试图解决从群数据中获得跨部分的反群问题。但是解决方案不一定是独一无二的。为了解决这一问题，我们研究了深度学习模型的使用，这些模型是使用弹性动量转移，电离和激发横截面的先前确定的，用于LXCAT网站上可用的不同气体及其相应的群，使用BOLSIG+求解器计算的相应的群参数用于Boltzmann方程在弱级别的弱级别ies iesized Queses中的电子方程。我们实施人工神经网络（ANN），卷积神经网络（CNN）和密集连接的卷积网络（Densenet）。据我们所知，尚无研究探索CNN和Densenet用于逆群问题的使用。我们测试了所有这些受过训练的网络为广泛的气体物种测试预测的有效性，并推断出densenet可以从群数据中有效提取长期和短期特征，因此，它可以预测与ANN相比，精度明显更高的横截面。此外，我们将Monte Carlo辍学作为贝叶斯近似，以估计横截面的概率分布，以确定该反问题的所有合理解决方案。
Electron-neutral scattering cross sections are fundamental quantities in simulations of low temperature plasmas used for many technological applications today. From these microscopic cross sections, several macro-scale quantities (called "swarm" parameters) can be calculated. However, measurements as well as theoretical calculations of cross sections are challenging. Since the 1960s researchers have attempted to solve the inverse swarm problem of obtaining cross sections from swarm data; but the solutions are not necessarily unique. To address this issues, we examine the use of deep learning models which are trained using the previous determinations of elastic momentum transfer, ionization and excitation cross sections for different gases available on the LXCat website and their corresponding swarm parameters calculated using the BOLSIG+ solver for the numerical solution of the Boltzmann equation for electrons in weakly ionized gases. We implement artificial neural network (ANN), convolutional neural network (CNN) and densely connected convolutional network (DenseNet) for this investigation. To the best of our knowledge, there is no study exploring the use of CNN and DenseNet for the inverse swarm problem. We test the validity of predictions by all these trained networks for a broad range of gas species and we deduce that DenseNet effectively extracts both long and short term features from the swarm data and hence, it predicts cross sections with significantly higher accuracy compared to ANN. Further, we apply Monte Carlo dropout as Bayesian approximation to estimate the probability distribution of the cross sections to determine all plausible solutions of this inverse problem.
