Physics-Informed Neural Networks for Depth-Averaged Granular Avalanche Dynamics on Curved Topography
Abstract
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 trajectory error by approximately two orders of magnitude compared with training over the full time domain from the outset. Sparse-data experiments further showed that observation placement was more influential than observation number within the configurations tested. Four observations bracketing the transition from acceleration to deceleration achieved nearly the same accuracy as the eight-observation reference configuration, whereas observations clustered at early or late times performed poorly. The results demonstrate the importance of both training strategy and informative data placement when applying PINNs to granular flows over curved topography.
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BibTeX
@misc{purohit2026physicsinformed,
title = {{Physics-Informed Neural Networks for Depth-Averaged Granular Avalanche Dynamics on Curved Topography}},
author = {Pujan Pranavkumar Purohit and Pradyumn Singh Sikarwar and Vishal Sharma and Gaurav Bhutani},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.05542},
eprint = {2609.05542},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.05542},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Physics-Informed Neural Networks for Depth-Averaged Granular Avalanche Dynamics on Curved Topography
AU - Pujan Pranavkumar Purohit
AU - Pradyumn Singh Sikarwar
AU - Vishal Sharma
AU - Gaurav Bhutani
PY - 2026
DA - 2026/09/02
PB - arXiv
DO - 10.48550/arxiv.2609.05542
UR - https://arxiv.org/abs/2609.05542
AB - 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 trajectory error by approximately two orders of magnitude compared with training over the full time domain from the outset. Sparse-data experiments further showed that observation placement was more influential than observation number within the configurations tested. Four observations bracketing the transition from acceleration to deceleration achieved nearly the same accuracy as the eight-observation reference configuration, whereas observations clustered at early or late times performed poorly. The results demonstrate the importance of both training strategy and informative data placement when applying PINNs to granular flows over curved topography.
ER -