基于Transformer的大气密度预报
Transformer-based Atmospheric Density Forecasting
- Massachusetts Institute of Technology（麻省理工学院）
- Universidad Politécnica de Madrid（马德里理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文针对空间态势感知中大气密度预报问题，提出基于Transformer的非线性预报架构，并与DMDc方法在NRLMSISE-00、JB2008和TIEGCM模型上进行比较，以改进传统线性传播方法的预报能力。
AI中文摘要：
随着2025年太阳活动周期峰值的临近，以及单次地磁暴能够显著改变驻留空间物体（RSOs）轨道的能力，大气密度预报技术对于空间态势感知至关重要。虽然线性数据驱动方法（例如带控制的动态模式分解，DMDc）此前已被用于大气密度预报，但基于深度学习的预报能够捕捉数据中的非线性特征。通过从历史大气密度数据中学习多层权重，数据集中的长期依赖关系被捕捉在当前大气密度状态与控制输入到下一时间步大气密度状态的映射中。本研究通过开发一种用于大气密度预报的非线性Transformer架构，改进了此前基于线性传播的大气密度预报方法。研究比较了经验模型NRLMSISE-00和JB2008，以及基于物理的TIEGCM大气密度模型，分别使用DMDc和基于Transformer的传播器进行预报。
英文摘要：
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.
