发表机构
Department of Mechanical, Materials and Aerospace Engineering, West Virginia University（西弗吉尼亚大学机械、材料与航空航天工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出SINDy-AR框架，用于稳定自回归非线性仿真TIEGCM热层密度，加速约9万倍，可部署于卫星，提升空间态势感知能力。
AI 中文摘要
近地轨道上卫星和空间驻留物体数量的持续增长，不断增加着这些物体之间碰撞的风险。为降低这一风险，运营者和科学家群体日益感到有必要开发新的空间态势感知工具以及新一代的热层概率仿真器。我们此前构建了一个基于三个步骤的降阶概率仿真器（ROPE）：降维、动态建模和不确定性量化。在此，我们在ROPE框架内进一步推进动态建模方面，采用一类受SINDy启发的稳定且自回归的非线性模型，SINDy是一个用于识别非线性动力学的框架。我们使用这一称为SINDy-AR的框架来仿真TIEGCM模拟的热层密度。这也是SINDy-AR框架首次应用于像热层仿真这样高维且复杂的系统。动态仿真器根据稳定性和准确性标准进行筛选，以确保其能在运行和空间态势感知应用中可靠使用。使用SINDy-AR构建替代模型可以展示所学仿真之间的动态差异。TIEGCM仿真的状态依赖结构显示出与存在三日潮汐波、与太阳自转相关的27天波、半年波和年潮汐波的兼容性。整个SINDy-AR流程相对于TIEGCM 2.0的执行加速比约为90,000倍，这使得新框架在低计算约束条件下能够部署在卫星上。
英文摘要
The ever-growing number of satellites and resident space objects in low Earth orbit keeps increasing the risk of collision between these objects. To mitigate this risk, the development of new space situational awareness tools and a new generation of probabilistic emulators of the thermosphere is increasingly felt by the community of operators and scientists. We previously built a Reduced Order Probabilistic Emulator based on 3 steps: dimensionality reduction, dynamic modeling, and uncertainty quantification. Here we further develop the dynamic modeling side within ROPE using a class of stable and autoregressive nonlinear models inspired by SINDy, a framework for the identification of the nonlinear dynamics. We use this framework, which we call SINDy-AR, to emulate the thermospheric density simulated by TIEGCM. This is also the first time a SINDy-AR framework is applied to such a high dimensional system as the thermospheric emulation because of its complexity. The dynamical emulators are selected on stability and accuracy criteria, so that they can be relied on in operations and space situational awareness applications. Building surrogate models with SINDy-AR can show dynamical differences between the learned emulations. The state-dependent structure of the TIEGCM emulation shows compatibility with the presence of terdiurnal, 27-day linked to solar rotation, semiannual, and annual tidal waves. The execution speedup of the whole SINDy-AR pipeline with respect to TIEGCM 2.0 is about 90,000 times, which makes the new framework capable of being deployed on board a satellite given the low computational constraints it would be subjected to.
