Efficient and physically consistent generation of 2D concrete mesostructures via a regressor‐guided diffusion framework

Concrete mesostructure modeling is critical for simulation‐driven material design, as it directly influences the accuracy of mechanical and durability analyses. However, conventional methods such as computed tomography–based reconstruction and aggregate sequential placement suffer from low efficiency and limited controllability. To address these challenges, this study proposes a regressor‐guided conditional diffusion framework for 2D concrete mesostructure generation. By transforming continuous physical parameters into dynamic gradient fields, a separately trained regressor guides the denoising process to ensure strong compliance with aggregate gradation and particle size constraints. Experimental results demonstrate that this method significantly outperforms traditional conditional embedding models in terms of constraint accuracy, reducing aggregate volume fraction and particle size distribution errors by 12.6% and 31.6%, respectively. Additionally, the proposed framework achieves over two orders of magnitude greater efficiency than existing numerical techniques. Its modular design and fine‐grained control capabilities establish a scalable, physically consistent solution for high‐fidelity mesostructure generation in concrete and beyond.