As the peak of the solar cycle approaches in 2025 and the ability of a single
geomagnetic storm to significantly alter the orbit of Resident Space Objects
(RSOs), techniques for atmospheric density forecasting are vital for space
situational awareness. While linear data-driven methods, such as dynamic mode
decomposition with control (DMDc), have been used previously for forecasting
atmospheric density, deep learning-based forecasting has the ability to capture
nonlinearities in data. By learning multiple layer weights from historical
atmospheric density data, long-term dependencies in the dataset are captured in
the mapping between the current atmospheric density state and control input to
the atmospheric density state at the next timestep. This work improves upon
previous linear propagation methods for atmospheric density forecasting, by
developing a nonlinear transformer-based architecture for atmospheric density
forecasting. Empirical NRLMSISE-00 and JB2008, as well as physics-based TIEGCM
atmospheric density models are compared for forecasting with DMDc and with the
transformer-based propagator.

2025 年太阳活动周期达到高峰时，单个地磁风暴对居住空间物体轨道的显著影响，大气密度预测技术对空间态势感知至关重要。本研究通过开发基于非线性转换器的架构，改进了以往用于大气密度预测的线性传播方法，通过从历史大气密度数据中学习多层权重，捕捉数据集中的长期依赖关系，实现了大气密度预测。同时，比较了以前使用的基于动态模式分解与控制（DMDc）和基于转换器的传播器的经验性 NRLMSISE-00 和 JB2008 以及基于物理的 TIEGCM 大气密度模型的预测效果。

基于 Transformer 的大气密度预测

Transformer-based Atmospheric Density Forecasting

Operational flare forecasting aims at providing predictions that can be used
to make decisions, typically at a daily scale, about the space weather impacts
of flare occurrence. This study shows that video-based deep learning can be
used for operational purposes when the training and validation sets used for
the network optimization are generated while accounting for the periodicity of
the solar cycle. Specifically, the paper describes an algorithm that can be
applied to build up sets of active regions that are balanced according to the
flare class rates associated to a specific cycle phase. These sets are used to
train and validate a Long-term Recurrent Convolutional Network made of a
combination of a convolutional neural network and a Long-Short Memory network.
The reliability of this approach is assessed in the case of two prediction
windows containing the solar storm of March 2015 and September 2017,
respectively.

本研究证明了，考虑太阳周期性的情况下，基于视频的深度学习可以用于操作性耀斑预测，并描述了一个用于构建平衡活动区集的算法来训练和验证包含卷积神经网络和长短期记忆网络的长期循环卷积网络，该方法以 2015 年 3 月和 2017 年 9 月的太阳风暴为例来评估其效果。