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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Vydané v:	2024 IEEE Congress on Evolutionary Computation (CEC) s. 1 - 9
Hlavní autori:	Licata, Richard J., Mehta, Piyush M.
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 30.06.2024
Predmet:	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
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