用数据驱动方法预测低轨卫星与碎片演化，速度快精度高。
A Data-Driven Approach to Estimate LEO Orbit Capacity Models
- 结合SINDy与LSTM建模低轨物体动态
- 基于MOCAT-MC数据生成快速预测模型
- 适合航天规划与空间碎片管理从业者
利用稀疏非线性动力学识别算法（SINDy）和长短期记忆循环神经网络（LSTM），可准确建模低地球轨道（LEO）中活跃卫星、废弃物体及碎片三类空间物体的种群演化，预测其未来传播。该方法基于高保真度计算模型MOCAT-MC生成的数据集，构建出轻量级、低保真度的替代模型，可在更短时间内实现高精度预测，适用于空间态势感知与轨道规划。
原文摘要 · Abstract (English)
Utilizing the Sparse Identification of Nonlinear Dynamics algorithm (SINDy) and Long Short-Term Memory Recurrent Neural Networks (LSTM), the population of resident space objects, divided into Active, Derelict, and Debris, in LEO can be accurately modeled to predict future satellite and debris propagation. This proposed approach makes use of a data set coming from a computational expensive high-fidelity model, the MOCAT-MC, to provide a light, low-fidelity counterpart that provides accurate forecasting in a shorter time frame.
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