Computer control of dynamic systems from the manufacturing, robotics, and aviation fields traditionally operate on timescales of 10s or 100s of milliseconds. For example, an avionics system traveling at 1000 kilometers per hour and operating at 10 milliseconds per control decision will move three meters in the time allocated to each control decision. However, emerging hypersonic, space, and military systems require active control while operating at extreme velocities or while being subjected to accelerations or decelerations caused by explosions or high-speed collisions. These applications require control at timescales on the order of microseconds. Making control decisions for such systems often requires that the controller estimate the state of the system from indirect measurements such as vibration. Traditional methods for state prediction are based on first principles using finite element analysis (FEA), whose execution time scales as a square of the number of elements. This makes it impractical to evaluate FEA models at microsecond timescales. Models derived from machine learning can estimate the state of the system based on pre-curated datasets and require less workload as compared to an equivalent FEA model. Such models, when combined with domain-specific processors, could provide equivalent accuracy with higher throughput than FEA models, making microsecond-scale state modeling possible. However, there are currently no suitable development methodologies for systematic generation of machine-learning models at such extreme performance constraints. The objective of this research is to develop a structural model compiler that meets a given accuracy constraint, as well as a corresponding overlay generator on which the generated model meets a given microsecond-scale latency constraint. This research will advance the fundamental knowledge and skills required for the real-time decision-making and control of active structures that experience high-rate dynamic events.