A physics-informed AI framework could help engineers rapidly design solid rocket grain structures that deliver targeted combustion behavior, although experimental validation is still needed. A recent study appearing as an article in press in the journal Communications Engineering introduces a generative artificial intelligence framework for the inverse design of three-dimensional energetic material structures with customizable combustion behavior. The researchers combined a Deep Eikonal Auto-Decoder, a Denoising Diffusion Probabilistic Model, and gradient-based optimization to generate structures that match target combustion profiles. The researchers built a multi-stage workflow, first constructing a large training dataset containing 47,800 three-dimensional energetic material structures derived from 42 representative grain geometries, which produced 4,763,200 valid structure-performance data pairs after filtering nonphysical chamber-pressure cases. The findings demonstrate a fast and effective simulation-based approach for designing energetic materials with tailored combustion profiles.