Researchers developed a hybrid generative AI framework that designs 3D energetic material grain structures to match user-defined pressure-time combustion profiles. By combining physics-informed geometry modeling, diffusion-based generation, and gradient-based refinement, the approach reduced design time while maintaining strong simulation-based performance matching.
