Physics-Informed Neural Networks for Depth-Averaged Granular Avalanche Dynamics on Curved Topography
Physics-informed neural networks (PINNs) provide a mesh-free framework for solving governing equations, but their application to granular avalanche dynamics over curved terrain remains largely unexplored. This study extends a depth-averaged PINN formulation based on the Savage-Hutter equations to an exponentially curved chute with spatially varying inclination and a strain-rate-dependent Mohr-Coulomb earth-pressure closure. The model is validated against measured front- and rear-edge trajectories from a laboratory granular-avalanche experiment, with selected observations withheld from training. A staged temporal curriculum proved essential for accurate prediction, reducing the held-out tr...
| Authors | Pujan Pranavkumar Purohit, Pradyumn Singh Sikarwar, Vishal Sharma et al. |
|---|---|
| Primary category | cond-mat.soft |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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