Physics-Regularized Generative Parameterization for Efficient Aerodynamic Shape Optimization

Aircraft design involves optimization within extremely high-dimensional geometric design spaces, posing a fundamental challenge for aerodynamic shape optimization. Although geometric filtering has improved design-space compactness compared to conventional parameterization methods, a compelling need remains to further shrink the design space without sacrificing design quality. This study introduces a physics-regularized generative parameterization that integrates data-driven aerodynamic modeling