Space domain awareness (SDA) comprises acquiring observations and processing those observations to perform orbit determination (OD), correlation and catalogue updates, tracking, predictions, and conjunction analysis. The existing SDA infrastructure provided by the US Space Surveillance Network (SSN) and the European Space Agency (ESA) Space Surveillance and Tracking (SST) is at its limit due to the continuous growth of Earth orbiting constellations, and the projected exponential growth of orbital debris (Kessler’s syndrome). In this talk, we will present our research developing the scientific framework to achieve rapid uncertainty quantification and propagation for space domain awareness and decision making. This enables domain awareness operations on the ground as well as onboard in real-time to resolve detected objects and predict their future locations. The present rapid uncertainty quantification combines model-based approximation techniques and data-driven approaches to quantify detected/tracked objects uncertainties in real-time. Results of the proposed model-driven uncertainty propagation approach have shown 2 – 3 orders of magnitude improvement over existing methods. The data-driven approach trained on the high-fidelity model is aimed to be implemented on spaceflight hardware. Highlighted in the present algorithm are the outcomes of the collaboration with professor Razaaly via the Chateubriand Fellowship.