Deep Learning Based Nonlinear Dimensionality Reduction for Emulators of Numerical Thermosphere Density Models

Modeling and forecasting of atmospheric drag for space objects in low Earth orbit (LEO) is a critical challenge for space situational awareness and environment safety and sustainability. The largest source of dynamics error or uncertainty affecting drag is the thermospheric density. Current operatio...

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Veröffentlicht in:	2024 IEEE Congress on Evolutionary Computation (CEC) S. 1 - 9
Hauptverfasser:	Licata, Richard J., Mehta, Piyush M.
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 30.06.2024
Schlagworte:	Atmospheric modeling Attention Computational modeling Computer architecture Convolutional Autoencoder Data models Dimensionality Reduction Low earth orbit satellites Orbital Drag Predictive models Space vehicles Thermospheric Density Emulator
Online-Zugang:	Volltext
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