Granular flows govern many natural and industrial processes, yet their interior kinematics and mechanics remain largely unobservable, as experiments access only boundaries or free surfaces. Conventional numerical simulations are computationally expensive for fast inverse reconstruction, and deterministic models tend to collapse to over-smoothed mean predictions in ill-posed settings. This study, to the best of the authors' knowledge, presents the first conditional flow matching (CFM) framework for granular-flow reconstruction from sparse boundary observations. Trained on high-fidelity particle-resolved discrete element simulations, the generative model is guided at inference by a differentiable forward operator with a sparsity-aware gradient guidance mechanism, which enforces measurement consistency without hyperparameter tuning and prevents unphysical velocity predictions in non-material regions. A physics decoder maps the reconstructed velocity fields to stress states and energy fluctuation quantities, including mean stress, deviatoric stress, and granular temperature. The framework accurately recovers interior flow fields from full observation to only 16% of the informative window, and it remains effective under strongly diluted spatial resolution with only 11% of data. It also outperforms a deterministic CNN baseline in the most ill-posed reconstruction regime and provides spatially resolved uncertainty estimates through ensemble generation. These results demonstrate that conditional generative modeling offers a practical route for non-invasive inference of hidden bulk mechanics in granular media, with broader applicability for inverse problems in particulate and multiphase systems.

翻译：颗粒流主导着许多自然和工业过程，但其内部运动学和力学特性在很大程度上仍无法直接观测，因为实验仅能触及边界或自由表面。传统数值模拟在快速反演重建中计算成本高昂，而确定性模型在不适定条件下往往会退化为过度平滑的均值预测。本研究（据作者所知）首次提出了基于条件流匹配的颗粒流反演框架，可从稀疏边界观测中重建颗粒流。该生成模型以高保真颗粒分辨离散元模拟数据为训练基础，在推理阶段通过可微正向算子与稀疏感知梯度引导机制进行指导，该机制无需超参数调优即可保证测量一致性，并能防止非材料区域出现非物理速度预测。物理解码器将重建的速度场映射为应力状态与能量波动量，包括平均应力、偏应力和颗粒温度。该框架能从完整观测到仅含16%信息窗口的数据中准确恢复内部流场，并且在空间分辨率严重稀释（仅11%数据）的条件下依然有效。在最不适定的重建场景下，它优于确定性CNN基线方法，并通过集成生成提供空间分辨的不确定性估计。这些结果表明，条件生成建模为颗粒介质中隐藏体力学特性的非侵入式推断提供了一条实用途径，并广泛适用于颗粒及多相系统中的反问题研究。