Physics-Informed AI Surrogate Models for Airworthiness Simulation

The focus of this STTR proposal is on using Physics-Informed Machine Learning (PIML) and surrogate modeling to reduce the bottleneck of High-Fidelity Aeroelastic Stability models that are required for any airworthiness certification.Traditional approaches to solving targeted problems such as aviation certification often require the use of physics-based modeling and simulation (M&S) in combination with subject matter expert(s) to advise on the usage and fidelity of their models and tools based on their experience. This is often a rinse and repeat process that is both unique and similar depending on the specific technical challenge. In the case of certification, we seek to build a digital expert ML/AI algorithm that can match the right technical objective with the correct digital models/tools with the optimal fidelity of those models. Approved for Public Release | 22-MDA-11339 (13 Dec 22)