Physics-Informed Neural Networks for Depth-Averaged Avalanche Dynamics
Accurate prediction of avalanche motion is essential for hazard assessment in mountainous terrain. This study develops and evaluates a physics-informed neural network (PINN) framework for the Savage-Hutter model of depth-averaged granular flow, progressing from 1D analytical verification to 2D experimental validation. First, three 1D problems of increasing complexity were verified against the analytical solution: height prediction with prescribed velocity, velocity prediction with prescribed height, and coupled prediction of both fields using the conservative formulation. The decoupled tests accurately reconstructed the spatio-temporal evolution of each field when the other was prescribed...
| Authors | Pradyumn Singh Sikarwar, Vishal Sharma, Gaurav Bhutani |
|---|---|
| Primary category | physics.flu-dyn |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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