Devise accurate and rapidly implementable AI design tools based on high fidelity solutions of the Navier-Stokes equations governing the unsteady transonic aerodynamics of aircraft wings with flutter constraints.

The proposed work will develop data modeling and radial basis function based machine learning (ML) models as AI tools trained on a small subset of the Navier-Stokes solutions corresponding to a selected space of geometric and flow parameters. These AI tools will then very rapidly identify the optimal design at any test point over this space of these parameters. The proposed effort exploits the ubiquitous geometrical nature of the unsteady transonic solution typical of flutter and will optimize the design of commercial aircraft wings.