[2604.04453] Generative modeling of granular flow on inclined planes using conditional flow matching

Computer Science > Computational Engineering, Finance, and Science arXiv:2604.04453 (cs) [Submitted on 6 Apr 2026] Title:Generative modeling of granular flow on inclined planes using conditional flow matching Authors:Xuyang Li, Rui Li, Teng Man, Yimin Lu View a PDF of the paper titled Generative modeling of granular flow on inclined planes using conditional flow matching, by Xuyang Li and 3 other authors View PDF HTML (experimental) Abstract: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, deviator...