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 and a novel sparsity-aware gradient guidance mechanism. This mechanism avoids the gradient dilution inherent to standard mean-squared-error approaches, preserves the absolute physical scale of observation errors, 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, and it suggests potential 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仍难以直接观测，因为实验手段仅能获取边界或自由表面的信息。传统数值模拟在快速逆重建中计算成本高昂，而确定性模型在不适定问题中往往退化为过度平滑的均值预测。本文首次提出基于条件流匹配（CFM）的颗粒流重建框架，通过稀疏边界观测数据实现内部状态重建。该生成模型基于高保真颗粒解析离散元模拟训练，推理时由可微前向算子及新型稀疏感知梯度引导机制进行调控。该机制可避免标准均方误差方法固有的梯度弥散问题，保留观测误差的绝对物理量级，无需超参数调优即可强制测量一致性约束，并防止非材料区域产生非物态速度预测。通过物理解码器将重建的速度场映射至应力状态与能量波动量（包括平均应力、偏应力和颗粒温度）。该框架在观测窗口内从完整观测到仅保留16%信息的情况下仍能准确恢复内部流场，并在空间分辨率严重稀释（仅11%数据）时保持有效。在最不适定的重建场景中，其性能优于确定性CNN基准模型，并通过集成生成提供空间分辨的不确定性估计。结果表明，条件生成模型为颗粒介质隐藏体态力学的非侵入式推断提供了可行路径，并有望应用于颗粒与多相系统中的逆问题求解。