Generative modeling of granular flow on inclined planes using conditional flow matching
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 differen...
| Authors | Xuyang Li, Rui Li, Teng Man et al. |
|---|---|
| Primary category | cs.CE |
| Published | 6 April 2026 |
| Last updated at source | 23 May 2026 |
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