论文标题
通过卷积神经网络识别2D混合Vlasov Maxwell模拟中的磁重新连接
Identifying magnetic reconnection in 2D Hybrid Vlasov Maxwell simulations with Convolutional Neural Networks
论文作者
论文摘要
磁重新连接是一个基本过程，可快速释放在等离子体中存储的磁能。从模拟输出中识别重新连接的模拟输出是非平凡的，通常必须由人类专家执行。因此，如果可以自动化这样的标识过程，那将是有价值的。在这里，我们证明了机器学习算法可以帮助识别无碰撞等离子体湍流模拟中的重新连接。使用Hybrid Vlasov Maxwell（HVM）模型，生成了包含2000多个潜在重新连接事件的数据集，并随后由人类专家标记。我们测试并将两种机器学习方法与此数据集上的不同配置进行了比较。最好的结果是通过卷积神经网络（CNN）以及一个“图像裁剪”步骤结合在潜在重新连接位点上的步骤。使用此方法，可以正确识别超过70％的重新连接事件。通过研究它们如何影响预测的准确性来评估不同物理变量的重要性。最后，我们还讨论了提出的模型中错误预测的各种可能原因。
Magnetic reconnection is a fundamental process that quickly releases magnetic energy stored in a plasma.Identifying, from simulation outputs, where reconnection is taking place is non-trivial and, in general, has to be performed by human experts. Hence, it would be valuable if such an identification process could be automated. Here, we demonstrate that a machine learning algorithm can help to identify reconnection in 2D simulations of collisionless plasma turbulence. Using a Hybrid Vlasov Maxwell (HVM) model, a data set containing over 2000 potential reconnection events was generated and subsequently labeled by human experts. We test and compare two machine learning approaches with different configurations on this data set. The best results are obtained with a convolutional neural network (CNN) combined with an 'image cropping' step that zooms in on potential reconnection sites. With this method, more than 70% of reconnection events can be identified correctly. The importance of different physical variables is evaluated by studying how they affect the accuracy of predictions. Finally, we also discuss various possible causes for wrong predictions from the proposed model.
