GDAnet enables cross-scale prediction of granular flow from static snapshots to dynamic space–time

Accurate prediction of granular flow in funnels remains a central challenge in granular mechanics and materials engineering. Conventional methods depend heavily on high-dimensional particle coordinate-velocity fields, which are costly to acquire, making it difficult to balance predictive accuracy and timeliness. This study constructs GDAnet, a deep learning framework that uses images as its input to extract key features of granular distributions from static snapshots and predict flow patterns across temporal intervals. Experimental results demonstrate that GDAnet achieves a coefficient of determination (R^2) of 0.992 in single-frame prediction tasks, substantially outperforming existing baseline models. In modeling temporal evolution, the framework achieves more than 90% precision in classifying various flow stages (e.g. falling, flowing, and blocking) and accurately predicts flow rate variations across different friction coefficients, both with and without clogging conditions. GDAnet overcomes the limitations of traditional numerical methods in computational efficiency and scalability through its image-to-motion learning paradigm, offering a novel technical pathway for granular flow research.