Machine Learned Atmospheric Force Model Training

This research article explores the development of a machine learning model to predict atmospheric drag on Low Earth Orbit (LEO) objects using Two-Line Elements (TLE) data. It discusses the significance of atmospheric modeling for accurate orbital predictions, the training process utilizing historical decay data, and regression tests comparing the ML model's performance against traditional propagation models. The findings aim to enhance the understanding of orbital decay due to atmospheric forces