As a common boundary layer that distinctly separates the regions of high‐density plasmasphere and low‐density plasmatrough, the plasmapause is essential to comprehend the dynamics and variability of the inner magnetosphere. Using the machine learning framework PyTorch and high‐quality Van Allen Probes data set, we develop a neural network model to predict the global dynamic variation of the plasmapause location, along with the identification of 6,537 plasmapause crossing events during the period from 2012 to 2017. To avoid the overfitting and optimize the model generalization, 5,493 events during the period from September 2012 to December 2015 are adopted for division into the training set and validation set in terms of the 10‐fold cross‐validation method, and the remaining 1,044 events are used as the test set. The model parameterized by only AE or Kp index can reproduce the plasmapause locations similar to those modeled using all five considered solar wind and geomagnetic parameters. Model evaluation on the test set indicates that our neural network model is capable of predicting the plasmapause location with the lowest RMSE. Our model can also produce a smooth magnetic local time variation of the plasmapause location with good accuracy, which can be incorporated into global radiation belt simulations and space weather forecasts under a variety of geomagnetic conditions.

中文翻译：

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使用神经网络预测血浆血浆动态位置

作为将高密度等离子球区和低密度等离子池区域分开的公共边界层，等离子暂停对于理解内部磁层的动力学和可变性是必不可少的。使用机器学习框架PyTorch和高质量的Van Allen Probes数据集，我们开发了一个神经网络模型来预测血浆暂停位置的全球动态变化，并确定2012年至2017年期间的6,537例血浆暂停交叉事件。为避免过拟合和优化模型泛化，根据10倍交叉验证方法，将2012年9月至2015年12月期间的5,493个事件划分为训练集和验证集，其余的1,044个事件为用作测试集。该模型仅由AE或Kp指数可以重现等离子体暂停的位置，类似于使用所有五个考虑的太阳风和地磁参数进行建模的位置。测试集上的模型评估表明，我们的神经网络模型能够以最低的RMSE预测血浆停滞的位置。我们的模型还可以精确地产生等离子暂停位置的平稳磁局部时间变化，可以将其合并到各种地磁条件下的全球辐射带模拟和太空天气预报中。