A Machine‐Learning‐Based Global Thermospheric Density Forecasting Model

Thermospheric mass density governs aerodynamic drag in low Earth orbit and is a primary source of uncertainty in orbit prediction and conjunction assessment, particularly during geomagnetic disturbances. We present AETHER‐ (Accelerometer‐driven Estimation of THERmospheric density–A Physics‐Informed Probabilistic Prediction Platform), a machine‐learning‐based global thermospheric density forecasting model that provides multi‐step forecasts up to 6 hr ahead using a 3‐hr input window, with predictive uncertainty estimates. AETHER‐ formulates thermospheric density forecasting as a sequence‐to‐sequence regression task conditioned on recent space weather evolution and a user‐specified sequence of future times and locations. To enhance physical consistency and generalization, AETHER‐ incorporates JB2008 and NRLMSISE‐00 density estimates evaluated at future locations, along with solar, geomagnetic, and solar‐wind drivers. The network employs dual recurrent encoders and an evidential Normal‐Gamma output head to jointly estimate forecast mean and uncertainty. The model is evaluated using independent satellite test cases spanning quiet, moderate, and extreme geomagnetic conditions. During quiet periods, AETHER‐ achieves high forecast skill . Under moderate activity, strong skill is retained , with reduced physical‐domain errors than empirical baseline models. During extreme storm conditions, deterministic forecast skill degrades as expected yet remains robust (–0.90). Predictive uncertainty remains well calibrated across all regimes. These results establish AETHER‐ as a practical, low‐latency, uncertainty‐aware capability for thermospheric density forecasting that supports orbit prediction, drag‐risk assessment, and operational decision‐making over its validated altitude range of approximately 300–520 km, with highest confidence in the data‐rich 400–520 km region.