Digital twinning enables real-time simulation and predictive modeling by maintaining a continuously updated virtual representation of a physical system. In mission-critical applications, such as mid-air collision avoidance, these models must operate online with extremely low latency to ensure safety. However, executing complex Model Recovery (MR) pipelines on edge devices is limited by computational and memory bandwidth constraints. This paper introduces a fast, FPGA-accelerated digital twinning framework that offloads key neural components, including gated recurrent units (GRU) and dense layers, to reconfigurable hardware for efficient parallel execution. Our system achieves real-time responsiveness, operating five times faster than typical human reaction time, and demonstrates the practical viability of deploying digital twins on edge platforms for time-sensitive, safety-critical environments.