Accurate and rapid predictions of the flow field surrounding high-speed flight vehicles are crucial for both early design stages and autonomous vehicle control. While data-driven model reduction offers a promising opportunity to leverage the accuracy of high-fidelity simulations for real-time applications, these methods are challenged when applied beyond the bounds of their training data or when data is sparse. Incorporating physical laws directly into the model training objective allows for the development of more robust representations tailored to address ill-posed inverse problems. Despite these advancements, the integration of multi-physics forward Partial Differential Equation (PDE) solvers with physics-informed reduced order models presents a challenge. Progress in physics-informed machine learning offer the potential to enhance early design phase analysis and support the development of lightweight models essential for onboard autonomous vehicle control. HiFlight explores physics-informed surrogate modeling for high-speed flight vehicles to make fast and accurate predictions of flight vehicle aerodynamic performance.